Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures | Paper Citation Record

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This paper cites Physics infused machine learning based predic- tion of vtol aerodynamics with sparse datasets,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics infused machine learning based predic- tion of vtol aerodynamics with sparse datasets,

Reference 1

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This paper cites A differentiable physics-informed machine learn- ing approach to model laser-based micro-manufacturing process,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A differentiable physics-informed machine learn- ing approach to model laser-based micro-manufacturing process,

Reference 2

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This paper cites Physics-informed machine learning towards a real-time spacecraft thermal simulator,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-informed machine learning towards a real-time spacecraft thermal simulator,

Reference 3

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This paper cites Physics-informed machine learning,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-informed machine learning,

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This paper cites Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Driven by data or derived through physics? a review of hybrid physics guided machine learning techniques with cyber-physical system (cps) focus,

Reference 5

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This paper cites Physics- informed neural networks for high-speed flows,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics- informed neural networks for high-speed flows,

Reference 6

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This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 7

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This paper cites Analyses of internal structures and defects in mate- rials using physics-informed neural networks,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Analyses of internal structures and defects in mate- rials using physics-informed neural networks,

Reference 8

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This paper cites Physics-informed neural networks (pinns) for fluid mechanics: A review,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-informed neural networks (pinns) for fluid mechanics: A review,

Reference 9

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Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Unresolved cited work

Reference 10

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This paper cites Physics-informed neural networks with periodic activation functions for solute transport in heterogeneous porous media,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-informed neural networks with periodic activation functions for solute transport in heterogeneous porous media,

Reference 11

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This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Fourier Neural Operator for Parametric Partial Differential Equations

Reference 12

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This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Learning nonlinear operators via deeponet based on the universal approximation theorem of operators,

Reference 13

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This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Scientific machine learning through physics–informed neural networks: Where we are and what’s next,

Reference 14

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This paper cites Investigating grey-box modeling for predictive analytics in smart manufacturing,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Investigating grey-box modeling for predictive analytics in smart manufacturing,

Reference 15

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This paper cites A grey box soft- ware framework for sustainability assessment of com- posed manufacturing processes: A hybrid manufactur- ing case,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A grey box soft- ware framework for sustainability assessment of com- posed manufacturing processes: A hybrid manufactur- ing case,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:09.579692Z

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This paper cites A physically based and machine learning hybrid approach for accu- rate rainfall-runoff modeling during extreme typhoon events,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A physically based and machine learning hybrid approach for accu- rate rainfall-runoff modeling during extreme typhoon events,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:09.349900Z

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This paper cites A com- bined neural-wavelet model for prediction of ligvanchai watershed precipitation,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A com- bined neural-wavelet model for prediction of ligvanchai watershed precipitation,

Reference 18

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Observation 229e6f17-b6d3-423d-abc8-50bbdc69d6f4 · outbound

This paper cites Pi-lstm: Physics-infused long short-term memory network,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Pi-lstm: Physics-infused long short-term memory network,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.892016Z

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This paper cites A robust & reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering.,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A robust & reliable data-driven prognostics approach based on extreme learning machine and fuzzy clustering.,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.742130Z

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This paper cites A fusion prognostics method for remaining useful life prediction of electronic prod- ucts,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A fusion prognostics method for remaining useful life prediction of electronic prod- ucts,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.514626Z

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Observation 2b863ee7-1403-46e0-9bb0-054a49aa24b2 · outbound

This paper cites Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

Reference 22

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This paper cites Interval static displace- ment analysis for structures with interval parameters,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Interval static displace- ment analysis for structures with interval parameters,

Reference 23

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This paper cites Im- proving interval analysis in finite element calculations by means of affine arithmetic,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Im- proving interval analysis in finite element calculations by means of affine arithmetic,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.063615Z

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Observation 90c6c2f2-8ad7-4bcf-9dc7-69aed498545e · outbound

This paper cites Uncertainty quantification in dynamic system risk assessment: A new approach with randomness and fuzzy theory,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Uncertainty quantification in dynamic system risk assessment: A new approach with randomness and fuzzy theory,

Reference 25

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This paper cites Modern monte carlo methods for efficient uncertainty quantification and propagation: A survey,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Modern monte carlo methods for efficient uncertainty quantification and propagation: A survey,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.662797Z

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This paper cites Bayesian neural networks for uncertainty quan- tification in data-driven materials modeling,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Bayesian neural networks for uncertainty quan- tification in data-driven materials modeling,

Reference 27

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Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Unresolved cited work

Reference 28

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This paper cites Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers

Reference 29

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This paper cites Flow field tomogra- phy with uncertainty quantification using a bayesian physics-informed neural network,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Flow field tomogra- phy with uncertainty quantification using a bayesian physics-informed neural network,

Reference 30

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This paper cites Bayesian physics-informed neural net- works for inverse uncertainty quantification problems in cardiac electrophysiology,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Bayesian physics-informed neural net- works for inverse uncertainty quantification problems in cardiac electrophysiology,

Reference 31

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This paper cites Physically interpretable machine learning for nu- clear masses,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physically interpretable machine learning for nu- clear masses,

Reference 32

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This paper cites Uncertainty quan- tification for additive manufacturing process improve- ment: Recent advances,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Uncertainty quan- tification for additive manufacturing process improve- ment: Recent advances,

Reference 33

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This paper cites Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Information fusion and machine learning for sensitivity analysis using physics knowledge and experimental data,

Reference 34

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This paper cites A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace,

Reference 35

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This paper cites Physics-informed machine learning for reliabil- ity and systems safety applications: State of the art and challenges,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Physics-informed machine learning for reliabil- ity and systems safety applications: State of the art and challenges,

Reference 36

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This paper cites Fuks, Physics Informed Machine Learning and Uncertainty Propagation for Multiphase Transport in Porous Media.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Fuks, Physics Informed Machine Learning and Uncertainty Propagation for Multiphase Transport in Porous Media

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:05.476906Z

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This paper cites Mul- tiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and gaussian process regression,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Mul- tiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and gaussian process regression,

Reference 38

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This paper cites Bayesian neural networks: An introduction and survey,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Bayesian neural networks: An introduction and survey,

Reference 39

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This paper cites What uncertainties do we need in bayesian deep learning for computer vision?.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures What uncertainties do we need in bayesian deep learning for computer vision?

Reference 40

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This paper cites Taking the human out of the loop: A review of bayesian optimization,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Taking the human out of the loop: A review of bayesian optimization,

Reference 41

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This paper cites Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables

Reference 42

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This paper cites Bayesian and markov chain monte carlo methods for identifying nonlinear systems in the presence of uncertainty,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Bayesian and markov chain monte carlo methods for identifying nonlinear systems in the presence of uncertainty,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:04.585068Z

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This paper cites Stochastic variational inference,.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Stochastic variational inference,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:04.278769Z

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Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Unresolved cited work

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This paper cites an unresolved cited work.

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures Unresolved cited work

Reference 46

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