Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel | Paper Citation Record

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel write newline

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel write newline

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel , " * write output.state after.block = add.period write newline

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel write newline

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This paper cites Exceptional cryogenic tensile properties of k4169 superalloy by micro-grain casting process.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Exceptional cryogenic tensile properties of k4169 superalloy by micro-grain casting process

Reference 5

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This paper cites Effects of mg17al12 phase on microstructure evolution and ductility in the az91 magnesium alloy during the continuous rheo-squeeze casting-extrusion process.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Effects of mg17al12 phase on microstructure evolution and ductility in the az91 magnesium alloy during the continuous rheo-squeeze casting-extrusion process

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This paper cites Grain size distribution and interfacial heat transfer coefficient during solidification of magnesium alloys using high pressure die casting process.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Grain size distribution and interfacial heat transfer coefficient during solidification of magnesium alloys using high pressure die casting process

Reference 7

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Observation 26754c32-e225-4fea-9e8e-cdc95a3d3c33 · outbound

This paper cites Sensor degradation in nuclear reactor pressure vessels: the overlooked factor in remaining useful life prediction.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Sensor degradation in nuclear reactor pressure vessels: the overlooked factor in remaining useful life prediction

Reference 8

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Observation 676437da-ce5f-4f98-a353-5086bc2086d9 · outbound

This paper cites Weight loss and burst testing investigations of sintered silicon carbide under oxidizing environments for next generation accident tolerant fuels for smr applications.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Weight loss and burst testing investigations of sintered silicon carbide under oxidizing environments for next generation accident tolerant fuels for smr applications

Reference 9

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This paper cites Microstructure and properties of novel al-ce-sc, al-ce-y, al-ce-zr and al-ce-sc-y alloy conductors processed by die casting, hot extrusion and cold drawing.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Microstructure and properties of novel al-ce-sc, al-ce-y, al-ce-zr and al-ce-sc-y alloy conductors processed by die casting, hot extrusion and cold drawing

Reference 10

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This paper cites Exceptional mechanical properties of az31 alloy wire by combination of cold drawing and ept.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Exceptional mechanical properties of az31 alloy wire by combination of cold drawing and ept

Reference 11

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This paper cites Grain evolution analysis and experimental validation in the extrusion of 6xxx alloys by use of a lagrangian fe code.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Grain evolution analysis and experimental validation in the extrusion of 6xxx alloys by use of a lagrangian fe code

Reference 12

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Observation 7f0c92de-74aa-4107-abf4-a67aea925d25 · outbound

This paper cites Crystal plasticity analysis of texture development in magnesium alloy during extrusion.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Crystal plasticity analysis of texture development in magnesium alloy during extrusion

Reference 13

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Observation 2d013a66-48d3-4fc1-bbcf-a901db3bb76f · outbound

This paper cites Predicting residual stress in a 316l electron beam weld joint incorporating plastic properties derived from a crystal plasticity finite element model.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Predicting residual stress in a 316l electron beam weld joint incorporating plastic properties derived from a crystal plasticity finite element model

Reference 14

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This paper cites Microstructure-based multiscale and heterogeneous elasto-plastic properties of 2205 duplex stainless steel welded joints: Experimental and modeling.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Microstructure-based multiscale and heterogeneous elasto-plastic properties of 2205 duplex stainless steel welded joints: Experimental and modeling

Reference 15

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Observation ca582c26-1345-41a6-bde5-7e7047fd4cd7 · outbound

This paper cites Tailored deformation behavior of 304l stainless steel through control of the crystallographic texture with laser-powder bed fusion.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Tailored deformation behavior of 304l stainless steel through control of the crystallographic texture with laser-powder bed fusion

Reference 16

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This paper cites Crystallographic texture evolution in bulk deformation processing of fcc metals.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Crystallographic texture evolution in bulk deformation processing of fcc metals

Reference 17

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This paper cites Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Machine learning-based multi-objective optimization for efficient identification of crystal plasticity model parameters

Reference 18

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This paper cites Improving mechanical properties of selective laser melted co29cr9w3cu alloy by eliminating mesh-like random high-angle grain boundary.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Improving mechanical properties of selective laser melted co29cr9w3cu alloy by eliminating mesh-like random high-angle grain boundary

Reference 19

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This paper cites Transition between low and high angle grain boundaries.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Transition between low and high angle grain boundaries

Reference 20

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This paper cites The response of dislocations, low angle grain boundaries and high angle grain boundaries at high strain rates.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel The response of dislocations, low angle grain boundaries and high angle grain boundaries at high strain rates

Reference 21

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Observation b92533f0-ebc0-41cd-8ea9-5bf6319c263c · outbound

This paper cites Localized brittle intergranular cracking and recrystallization-induced blunting in fatigue crack growth of ductile tantalum.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Localized brittle intergranular cracking and recrystallization-induced blunting in fatigue crack growth of ductile tantalum

Reference 22

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This paper cites Mechanics and mechanisms of fatigue damage and crack growth in advanced materials.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Mechanics and mechanisms of fatigue damage and crack growth in advanced materials

Reference 23

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Observation efacde8e-c23b-4b16-9cb6-8dd71381757a · outbound

This paper cites Variational gradient plasticity at finite strains.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Variational gradient plasticity at finite strains

Reference 24

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Observation b991592c-e5ea-4308-8799-94c6f16f09eb · outbound

This paper cites Crystal plasticity simulations with representative volume element of as-build laser powder bed fusion materials.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Crystal plasticity simulations with representative volume element of as-build laser powder bed fusion materials

Reference 25

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Observation 3eb91c3a-8c55-4d88-a7a5-16022253ffd0 · outbound

This paper cites A virtual laboratory based on full-field crystal plasticity simulation to characterize the multiscale mechanical properties of ahss.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A virtual laboratory based on full-field crystal plasticity simulation to characterize the multiscale mechanical properties of ahss

Reference 26

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Observation 47b406d5-4367-4dc5-9692-392ad94d880d · outbound

This paper cites The inverse band-structure problem of finding an atomic configuration with given electronic properties.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel The inverse band-structure problem of finding an atomic configuration with given electronic properties

Reference 27

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Machine learning for molecular and materials science

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This paper cites Materials discovery and design using machine learning.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Materials discovery and design using machine learning

Reference 29

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This paper cites Grain-orientation induced stress formation in aa2024 monocrystal and bicrystal using crystal plasticity finite element method.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Grain-orientation induced stress formation in aa2024 monocrystal and bicrystal using crystal plasticity finite element method

Reference 30

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Machine learning-enabled self-consistent parametrically-upscaled crystal plasticity model for ni-based superalloys

Reference 31

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Observation 443767a7-f868-42f1-899c-17e37bf10ecb · outbound

This paper cites Micro-mechanisms of anisotropic deformation in the presence of notch in commercially pure titanium: An in-situ study with cpfem simulations.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Micro-mechanisms of anisotropic deformation in the presence of notch in commercially pure titanium: An in-situ study with cpfem simulations

Reference 32

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Observation 28d3adcc-3197-469f-9469-b0f3eabfebab · outbound

This paper cites Modeling cyclic deformation of inconel 718 superalloy by means of crystal plasticity and computational homogenization.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Modeling cyclic deformation of inconel 718 superalloy by means of crystal plasticity and computational homogenization

Reference 33

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This paper cites Investigating grain-resolved evolution of lattice strains during plasticity and creep using 3dxrd and crystal plasticity modelling.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Investigating grain-resolved evolution of lattice strains during plasticity and creep using 3dxrd and crystal plasticity modelling

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:03.048219Z

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Observation 263d4828-c0f9-4964-a9f3-9587b4427405 · outbound

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Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Improved generalization with deep neural operators for engineering systems: Path towards digital twin

Reference 35

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Observation af8230b1-e325-491b-8037-6c91d739f475 · outbound

This paper cites Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:02.521020Z

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Observation d96fbf80-fd55-463f-9391-4a31ea013c53 · outbound

This paper cites A machine learning model to predict yield surfaces from crystal plasticity simulations.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A machine learning model to predict yield surfaces from crystal plasticity simulations

Reference 37

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Observation ca80edbe-7b15-454a-af43-60fdf20b9593 · outbound

This paper cites Data-driven inverse design of monbtivwzr refractory multicomponent alloys: Microstructure and mechanical properties.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Data-driven inverse design of monbtivwzr refractory multicomponent alloys: Microstructure and mechanical properties

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:02.074099Z

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Observation 18dcd013-c80d-401f-a8a6-b345f33e695a · outbound

This paper cites Explainable, interpretable, and trustworthy AI for an intelligent digital twin: A case study on remaining useful life.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Explainable, interpretable, and trustworthy AI for an intelligent digital twin: A case study on remaining useful life

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:01.780297Z

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Observation 7315672b-642a-49e0-8d3e-bf9c312f9a62 · outbound

This paper cites Transfer learning with spinally shared layers.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Transfer learning with spinally shared layers

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:01.451528Z

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Observation 40e432f4-804a-4f88-8536-3ee68f1b1ae7 · outbound

This paper cites Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Digital twin-centered hybrid data-driven multi-stage deep learning framework for enhanced nuclear reactor power prediction

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:01.201238Z

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Observation 2cf5ae65-3528-459f-a411-2c3113f75a86 · outbound

This paper cites Physics-regularized neural networks for predictive modeling of silicon carbide swelling with limited experimental data.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Physics-regularized neural networks for predictive modeling of silicon carbide swelling with limited experimental data

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:00.931739Z

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Observation 87abad8e-4a65-4ccb-8f6b-26cdff048708 · outbound

This paper cites A generic high-throughput microstructure classification and quantification method for regular sem images of complex steel microstructures combining ebsd labeling and deep learning.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A generic high-throughput microstructure classification and quantification method for regular sem images of complex steel microstructures combining ebsd labeling and deep learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:00.661274Z

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Observation 412ed6c5-25d4-4574-86ab-3afe5aecd040 · outbound

This paper cites Unveiling the quantitative relationship between microstructural features and quasi-static tensile properties in dual-phase titanium alloys based on data-driven neural networks.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Unveiling the quantitative relationship between microstructural features and quasi-static tensile properties in dual-phase titanium alloys based on data-driven neural networks

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:00.339345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c21f5b92-22c4-47ea-994b-25c199eb68db · outbound

This paper cites Gaussian process regression as a surrogate model for the computation of dispersion relations.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Gaussian process regression as a surrogate model for the computation of dispersion relations

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:29:00.094501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b1c471c3-81d0-48f7-b0e9-4042ec37ed46 · outbound

This paper cites Gaussian process regressions on hot deformation behaviors of fgh98 nickel-based powder superalloy.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Gaussian process regressions on hot deformation behaviors of fgh98 nickel-based powder superalloy

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:59.876305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9311ccee-15a2-4590-8933-9734fc464464 · outbound

This paper cites Practical applications of gaussian process with uncertainty quantification and sensitivity analysis for digital twin for accident-tolerant fuel.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Practical applications of gaussian process with uncertainty quantification and sensitivity analysis for digital twin for accident-tolerant fuel

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:59.646656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dc3d7bd8-d36e-4fab-9840-f43b255f874c · outbound

This paper cites Global sensitivity analysis using polynomial chaos expansions.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Global sensitivity analysis using polynomial chaos expansions

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:59.396896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2d21d100-6258-4c41-b053-bd1fbbd43f23 · outbound

This paper cites Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Surrogate modeling of high-dimensional problems via data-driven polynomial chaos expansions and sparse partial least square

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:59.186066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c6691c8-1b38-4144-b544-b95e732d9b9c · outbound

This paper cites Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Data-driven multi-scale modeling and robust optimization of composite structure with uncertainty quantification

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:58.958575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6812e811-0879-476e-b771-2ee25a8a28b8 · outbound

This paper cites Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Recent advances in machine learning-assisted fatigue life prediction of additive manufactured metallic materials: A review

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:58.707330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5f7abae6-0d38-46b7-bd38-feedc3e3b85a · outbound

This paper cites Bayesian analysis of parametric uncertainties and model form probabilities for two different crystal plasticity models of lamellar grains in + titanium alloys.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Bayesian analysis of parametric uncertainties and model form probabilities for two different crystal plasticity models of lamellar grains in + titanium alloys

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:58.484052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2dd48706-dc89-421f-ba67-3d4a9c0d30b6 · outbound

This paper cites A predictive machine learning approach for microstructure optimization and materials design.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A predictive machine learning approach for microstructure optimization and materials design

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:58.261535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d014512b-3d3d-486a-86b4-88d1686d9612 · outbound

This paper cites Extracting dislocation microstructures by deep learning.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Extracting dislocation microstructures by deep learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:58.068762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b7156fc7-c15d-4482-b315-1253a5462324 · outbound

This paper cites Structural health monitoring: a machine learning perspective.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Structural health monitoring: a machine learning perspective

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:41.882679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6584fb60-0ba1-4057-84d2-ab1227af64cd · outbound

This paper cites Machine learning strategy for accelerated design of polymer dielectrics.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Machine learning strategy for accelerated design of polymer dielectrics

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:57.837902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 64fe39d6-3d74-48e0-86ad-a10d1f5905ad · outbound

This paper cites A review of the application of machine learning and data mining approaches in continuum materials mechanics.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A review of the application of machine learning and data mining approaches in continuum materials mechanics

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:57.648065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4cf83fa8-220b-4e08-b916-2823d2776347 · outbound

This paper cites Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Data-driven reduced-order models for rank-ordering the high cycle fatigue performance of polycrystalline microstructures

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:57.396659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9d2c9e82-5707-4302-b34c-cfd6a115967b · outbound

This paper cites Combining a neural network with a genetic algorithm for process parameter optimization.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Combining a neural network with a genetic algorithm for process parameter optimization

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:57.203009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13e396e3-5967-416a-8dad-6db4ee2fe8b5 · outbound

This paper cites Understanding of additively manufactured material cyclic behavior at the grain scale by neutron diffraction and crystal plasticity modeling.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Understanding of additively manufactured material cyclic behavior at the grain scale by neutron diffraction and crystal plasticity modeling

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:56.960550Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5cec029e-715f-4b84-95f6-1aafe78fde18 · outbound

This paper cites Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:56.756853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2df134d9-871c-4ef6-be37-b536e53e391c · outbound

This paper cites Real-time coherent diffraction inversion using deep generative networks.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Real-time coherent diffraction inversion using deep generative networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:56.459680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b90077b-deff-47da-b68d-7f2f4c933684 · outbound

This paper cites Applied machine learning to predict stress hotspots i: Face centered cubic materials.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Applied machine learning to predict stress hotspots i: Face centered cubic materials

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:56.246191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7c58876-6d66-48f5-b347-1f6af20657b8 · outbound

This paper cites Smart finite elements: A novel machine learning application.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Smart finite elements: A novel machine learning application

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:55.955467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c79e1f0-15da-43dc-a7a6-806cd4c2e365 · outbound

This paper cites Stress--strain curve predictions by crystal plasticity simulations and machine learning.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Stress--strain curve predictions by crystal plasticity simulations and machine learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:55.709095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 56e12223-0c65-4142-bf14-a02d233e5411 · outbound

This paper cites Application of artificial neural networks in micromechanics for polycrystalline metals.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Application of artificial neural networks in micromechanics for polycrystalline metals

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:55.452789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e396b447-71fa-4294-ae01-ceba562eec43 · outbound

This paper cites An uncertainty quantification framework for multiscale parametrically homogenized constitutive models (phcms) of polycrystalline ti alloys.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel An uncertainty quantification framework for multiscale parametrically homogenized constitutive models (phcms) of polycrystalline ti alloys

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:55.214116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4dcc9c8b-254a-413d-8224-ea7cc43fd61c · outbound

This paper cites Grain size and shape dependent crystal plasticity finite element model and its application to electron beam welded ss316l.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Grain size and shape dependent crystal plasticity finite element model and its application to electron beam welded ss316l

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:55.013746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c4596ca8-99a3-41aa-a985-602ba4461089 · outbound

This paper cites Kinetics of flow and strain-hardening.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Kinetics of flow and strain-hardening

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:54.798028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6fc3758-6c48-4dda-b007-4a9ffa8bd658 · outbound

This paper cites A unified phenomenological description of work hardening and creep based on one-parameter models.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A unified phenomenological description of work hardening and creep based on one-parameter models

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:54.510430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e9458335-1978-41bc-88e0-3f01a7aef201 · outbound

This paper cites A mathematical representation of the multiaxial Bauschinger effect, volume 731.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel A mathematical representation of the multiaxial Bauschinger effect, volume 731

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:54.304104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32f4c1e4-4776-41ba-9bf3-ff934b82b3a4 · outbound

This paper cites Three dimensional predictions of grain scale plasticity and grain boundaries using crystal plasticity finite element models.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Three dimensional predictions of grain scale plasticity and grain boundaries using crystal plasticity finite element models

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:54.057124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c7e4a69-edc3-4ca5-bea9-ffdc5106897b · outbound

This paper cites Crystal plasticity finite element modeling of extension twinning in we43 mg alloys: calibration and validation.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Crystal plasticity finite element modeling of extension twinning in we43 mg alloys: calibration and validation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:53.816075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 642eaa27-63cf-42da-bd61-e00e6cf406f9 · outbound

This paper cites Multiscale stress and strain statistics in the deformation of polycrystalline alloys.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Multiscale stress and strain statistics in the deformation of polycrystalline alloys

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:53.630084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9f97ae20-d43d-43d0-993b-07e35d160a8a · outbound

This paper cites Investigating mesh sensitivity and polycrystalline rves in crystal plasticity finite element simulations.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Investigating mesh sensitivity and polycrystalline rves in crystal plasticity finite element simulations

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:53.383667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation abc07078-299c-4c02-85c6-57bd916cd0cc · outbound

This paper cites Managing computational complexity using surrogate models: a critical review.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Managing computational complexity using surrogate models: a critical review

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:53.158628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0efc5ca6-e6d4-47a6-a040-30eeb5e57d82 · outbound

This paper cites Engineering design via surrogate modelling: a practical guide.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Engineering design via surrogate modelling: a practical guide

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:44.213087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95407e12-33d3-41cd-902d-55b2f437dae4 · outbound

This paper cites Building surrogate models based on detailed and approximate simulations.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Building surrogate models based on detailed and approximate simulations

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:52.868012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f063cea8-92f0-4ff5-bffb-021e9f20cd53 · outbound

This paper cites The homogeneous chaos.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel The homogeneous chaos

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:52.629336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77ccfb3b-44db-42c9-b8c0-dafd6703bcd3 · outbound

This paper cites The wiener--askey polynomial chaos for stochastic differential equations.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel The wiener--askey polynomial chaos for stochastic differential equations

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:44.518471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0db883e-a3dc-46f4-a297-3ea185a600cc · outbound

This paper cites Quantitative risk assessment of a high power density small modular reactor ( SMR ) core using uncertainty and sensitivity analyses.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Quantitative risk assessment of a high power density small modular reactor ( SMR ) core using uncertainty and sensitivity analyses

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:52.400564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f053f4c1-30c1-43ce-8b23-b2ed4cd9b89b · outbound

This paper cites Multi-criteria decision making under uncertainties in composite materials selection and design.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Multi-criteria decision making under uncertainties in composite materials selection and design

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:52.211737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9860659d-edcd-4894-9676-0ee1a1214c8d · outbound

This paper cites Uncertainty quantification and sensitivity analysis for digital twin enabling technology.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Uncertainty quantification and sensitivity analysis for digital twin enabling technology

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:51.959895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2535e6c7-2920-4fc1-a953-f35d19462fbd · outbound

This paper cites Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Sparse polynomial chaos expansions and adaptive stochastic finite elements using a regression approach

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:51.722729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 632f41d1-fafd-40af-bcd1-70b28c93932f · outbound

This paper cites An efficient non-intrusive reduced basis model for high dimensional stochastic problems in CFD.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel An efficient non-intrusive reduced basis model for high dimensional stochastic problems in CFD

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:51.386668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d21b37f1-b202-487d-8cf0-24a42eb8949f · outbound

This paper cites Efficient uncertainty quantification and management in the early stage design of composite applications.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Efficient uncertainty quantification and management in the early stage design of composite applications

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:51.147374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4e88737d-0ceb-4d5f-9d59-f54f136a6b4e · outbound

This paper cites Recent advances in uncertainty quantification methods for engineering problems.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Recent advances in uncertainty quantification methods for engineering problems

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:50.885655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e0f257b5-9522-4609-aee8-ea66c134d99b · outbound

This paper cites Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:50.641708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7068e19-98ef-4fc9-bda5-9a3e536f3936 · outbound

This paper cites AI -driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel AI -driven uncertainty quantification & multi-physics approach to evaluate cladding materials in a microreactor

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:50.321579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6301d27c-6d7f-4146-a305-fd6abf849ff4 · outbound

This paper cites AI -driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel AI -driven non-intrusive uncertainty quantification of advanced nuclear fuels for digital twin-enabling technology

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:50.068702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3648c89a-b2e8-4786-84e4-1adb0fb5f246 · outbound

This paper cites Error estimation and adaptive mesh refinement in boundary element method, an overview.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Error estimation and adaptive mesh refinement in boundary element method, an overview

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:49.840743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 45f17821-e3c9-488d-bc86-c208b8c65a43 · outbound

This paper cites Adaptive mesh refinement method for optimal control using nonsmoothness detection and mesh size reduction.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Adaptive mesh refinement method for optimal control using nonsmoothness detection and mesh size reduction

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:49.439798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c8ce8bb-a040-4dec-9dac-e925a9f15527 · outbound

This paper cites Isogeometric analysis: Cad, finite elements, nurbs, exact geometry and mesh refinement.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Isogeometric analysis: Cad, finite elements, nurbs, exact geometry and mesh refinement

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:49.153245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7657b8b0-6bd8-4e57-9870-dc61671e3b3f · outbound

This paper cites The inclusion and role of micro mechanical residual stress on deformation of stainless steel type 316l at grain level.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel The inclusion and role of micro mechanical residual stress on deformation of stainless steel type 316l at grain level

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:48.881621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a379fc4-7d30-416c-9942-45260183877e · outbound

This paper cites Stress fields and geometrically necessary dislocation density distributions near the head of a blocked slip band.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Stress fields and geometrically necessary dislocation density distributions near the head of a blocked slip band

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:48.685094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23bb4fe6-bc0a-4eaf-8002-76e068b4ad61 · outbound

This paper cites On the measurement of dislocations and dislocation substructures using ebsd and hrsd techniques.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel On the measurement of dislocations and dislocation substructures using ebsd and hrsd techniques

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:48.420715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b83f1aa-db9a-4dc6-a746-452fe5410bda · outbound

This paper cites Experimental measurement of dislocation density in metallic materials: A quantitative comparison between measurements techniques (xrd, r-ecci, hr-ebsd, tem).

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Experimental measurement of dislocation density in metallic materials: A quantitative comparison between measurements techniques (xrd, r-ecci, hr-ebsd, tem)

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:48.161483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dbf70327-02c1-486e-bb45-074b9b51d489 · outbound

This paper cites Methods and guidelines for effective model calibration.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Methods and guidelines for effective model calibration

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:47.887319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c962544b-7faf-4891-ab75-8232f7b2e4f6 · outbound

This paper cites The future of distributed models: model calibration and uncertainty prediction.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel The future of distributed models: model calibration and uncertainty prediction

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:47.633326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 544dcd07-54cc-45ba-a6dc-7ce41f001413 · outbound

This paper cites Crystal plasticity modeling of deformation and creep in polycrystalline ti-6242.

Degradation-Aware and Machine Learning-Driven Uncertainty Quantification in Crystal Plasticity Finite Element: Texture-Driven Plasticity in 316L Stainless Steel Crystal plasticity modeling of deformation and creep in polycrystalline ti-6242

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:47.362739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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