Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling | Paper Citation Record

Observation 26e73d42-16aa-495d-a9a6-4a3fb2d0d1e6 · outbound

This paper cites Artificial neural networks for solving ordinary and partial differential equations.IEEE transactions on neural networks, 9(5):987–1000, 1998.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Artificial neural networks for solving ordinary and partial differential equations.IEEE transactions on neural networks, 9(5):987–1000, 1998

Reference 1

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Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 2

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Observation c00689c8-7329-4f60-9c9b-64e549c73ae4 · outbound

This paper cites Deep learning.nature, 521(7553):436–444, 2015.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Deep learning.nature, 521(7553):436–444, 2015

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:44.880430Z

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Observation f960c4f2-ad36-442a-8450-b6844b1a3e62 · outbound

This paper cites Ghaboussi, J.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Ghaboussi, J

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:44.634574Z

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This paper cites Bessa, R.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Bessa, R

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:44.424208Z

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This paper cites Computational homogenization of nonlinear elastic materials using neural networks.International Journal for Numerical Methods in Engineering, 104(12):1061–1084, 2015.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Computational homogenization of nonlinear elastic materials using neural networks.International Journal for Numerical Methods in Engineering, 104(12):1061–1084, 2015

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:44.078150Z

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This paper cites Bessa, and Wing Kam Liu.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Bessa, and Wing Kam Liu

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:43.816414Z

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Observation a92d2966-ec19-4ae9-a1a7-fda663dde798 · outbound

This paper cites Mozaffar, R.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Mozaffar, R

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:43.504849Z

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This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:43.209423Z

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Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:42.884308Z

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Observation d69dece2-52c6-4606-93f4-8f5877ccdcaa · outbound

This paper cites A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity.Computers and Geotechnics, 159:105472, 2023.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity.Computers and Geotechnics, 159:105472, 2023

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:42.632090Z

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Observation fb76fc0d-80ed-4e9f-9c3a-462e9c53f5fa · outbound

This paper cites Kalina, Jörg Brummund, WaiChing Sun, and Markus Kästner.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Kalina, Jörg Brummund, WaiChing Sun, and Markus Kästner

Reference 12

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Observation 61fd1421-5e8e-4a9a-8c9d-3472f5e197b9 · outbound

This paper cites Automated model discovery of finite strain elastoplasticity from uniaxial experiments.Computer Methods in Applied Mechanics and Engineering, 435:117653, 2025.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Automated model discovery of finite strain elastoplasticity from uniaxial experiments.Computer Methods in Applied Mechanics and Engineering, 435:117653, 2025

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:24.787329Z

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This paper cites Consistent machine learning for topology optimization with microstructure-dependent neural network material models.Journal of the Mechanics and Physics of Solids, 196:106015, 2025.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Consistent machine learning for topology optimization with microstructure-dependent neural network material models.Journal of the Mechanics and Physics of Solids, 196:106015, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:24.864760Z

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Observation c7f2fcee-9bd5-45ce-84a1-bef5347f9131 · outbound

This paper cites Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:24.930442Z

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Observation 5000da21-f23e-4b3a-b705-8e7ee7714a66 · outbound

This paper cites Ferreira, F.M.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Ferreira, F.M

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:42.347672Z

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Observation c38369f6-9ced-40b7-8b17-d932420bd8cc · outbound

This paper cites Ferreira, F.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Ferreira, F

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:42.013856Z

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Observation ac961b3f-8440-4f37-812e-36ab6061664f · outbound

This paper cites Deep Bayesian active learning with image data.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Deep Bayesian active learning with image data

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:41.688935Z

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This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:41.351722Z

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This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:41.019767Z

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Observation 8c01ad93-cd56-498d-8269-bbdb0094f2a6 · outbound

This paper cites Bayesian deep learning and a probabilistic perspective of generalization.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Bayesian deep learning and a probabilistic perspective of generalization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:40.728516Z

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Observation 3613d8ff-413f-47dd-8281-ced32ef4fcd7 · outbound

This paper cites Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:40.497522Z

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Observation a250179f-1180-481c-a53a-ec3bef8bd35b · outbound

This paper cites Psaros, Xuhui Meng, Zongren Zou, Ling Guo, and George Em Karniadakis.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Psaros, Xuhui Meng, Zongren Zou, Ling Guo, and George Em Karniadakis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:40.174008Z

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This paper cites Bessa, Piotr Glowacki, and Michael Houlder.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Bessa, Piotr Glowacki, and Michael Houlder

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:39.917496Z

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This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:39.630748Z

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Observation 9092fd3f-0e0e-43d8-9942-0cfa52424a2a · outbound

This paper cites McDowell.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling McDowell

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:39.342154Z

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Observation d6f19cdd-d542-49a6-be4d-31fe2b086746 · outbound

This paper cites MIT press Cambridge, MA, 2006.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling MIT press Cambridge, MA, 2006

Reference 27

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Observation de8f1a4e-1787-429b-a8b9-5260ebdfd953 · outbound

This paper cites Heteroscedastic gaussian process regression for material structure-property relationship modeling.Computer Methods in Applied Mechanics and Engineering, 431:117326, 2024.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Heteroscedastic gaussian process regression for material structure-property relationship modeling.Computer Methods in Applied Mechanics and Engineering, 431:117326, 2024

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:39.055100Z

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Observation fb689dbf-dc91-46bd-b7af-5544ca217cd0 · outbound

This paper cites Mcmc using hamiltonian dynamics.Handbook of markov chain monte carlo, 2(11):2, 2011.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Mcmc using hamiltonian dynamics.Handbook of markov chain monte carlo, 2(11):2, 2011

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:38.740226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59b9eaba-a2c5-4c62-aaee-b174e09e4223 · outbound

This paper cites Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:26.637436Z

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Observation a7b07c9b-f738-4a3a-897f-566a675bf198 · outbound

This paper cites Springer Science & Business Media, 2012.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Springer Science & Business Media, 2012

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:26.724206Z

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Observation 974fb2bf-fc5b-443f-b73a-faf0905971a8 · outbound

This paper cites Blei, Alp Kucukelbir, and Jon D.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Blei, Alp Kucukelbir, and Jon D

Reference 32

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Observation 72ae02cc-368a-4daa-adb6-571d1bb9192d · outbound

This paper cites Dropout as a bayesian approximation: Representing model uncertainty in deep learning.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Dropout as a bayesian approximation: Representing model uncertainty in deep learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:38.467051Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 942a794a-2122-473e-8ec7-a1750aef79ee · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:38.236698Z

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Observation 39bc4765-5c85-4f8a-a959-b643da8b2ab5 · outbound

This paper cites Shields, and Lori Graham-Brady.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Shields, and Lori Graham-Brady

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:37.931563Z

Source-reported events for the cited work

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Observation c9e00204-0c60-4271-9cef-bca11e4b52c4 · outbound

This paper cites Pasparakis, Lori Graham-Brady, and Michael D.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Pasparakis, Lori Graham-Brady, and Michael D

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:37.669604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a541512c-31b8-4624-b33c-8c1a91d3e0ea · outbound

This paper cites Discovering uncertainty: Bayesian constitutive artificial neural networks.Computer Methods in Applied Mechanics and Engineering, 433:117517, 2025.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Discovering uncertainty: Bayesian constitutive artificial neural networks.Computer Methods in Applied Mechanics and Engineering, 433:117517, 2025

Reference 37

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Observation 83d83c85-291a-4e61-ba0f-e8b9bbd079d5 · outbound

This paper cites Sparse bayesian recurrent neural networks.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Sparse bayesian recurrent neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:37.350007Z

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Observation e4fb79a0-0ee6-4197-8ab6-178fd68628ec · outbound

This paper cites Bayesian recurrent neural networks, 2017.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Bayesian recurrent neural networks, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:36.992963Z

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Observation 7d333a8e-3623-4148-9ae0-8a6c226b3cd2 · outbound

This paper cites Scalable bayesian learning of recurrent neural networks for language modeling, 2017.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Scalable bayesian learning of recurrent neural networks for language modeling, 2017

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:36.715062Z

Source-reported events for the cited work

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Observation 0b41d0e8-919f-4df3-b5ac-b6e040d58e18 · outbound

This paper cites BARNN: A Bayesian Autoregressive and Recurrent Neural Network.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling BARNN: A Bayesian Autoregressive and Recurrent Neural Network

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Resolution: verified exact; local_arxiv, observed 2026-08-06T16:38:30.513769Z

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Observation 7797bc57-a459-4ae1-bad8-8d8319a7b70f · outbound

This paper cites Preconditioned stochastic gradient langevin dynamics for deep neural networks, 2015.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Preconditioned stochastic gradient langevin dynamics for deep neural networks, 2015

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:36.451374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b6e06fd1-8fe3-4a2a-8cb2-3603fcf8d63e · outbound

This paper cites Estimating the mean and variance of the target probability distribution.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Estimating the mean and variance of the target probability distribution

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:36.142210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 43a9e82d-096e-4bea-b3d0-0e1a61d9d251 · outbound

This paper cites A multi-fidelity machine learning approach to high throughput materials screening.npj Computational Materials, 8(1):257, 2022.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling A multi-fidelity machine learning approach to high throughput materials screening.npj Computational Materials, 8(1):257, 2022

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:35.872909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 17d5426c-89c8-43ee-b9b0-71fce6b846c9 · outbound

This paper cites Haftka, and Nam H.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Haftka, and Nam H

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:35.556907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e0b70a3f-9193-432b-8c5f-9cc5b2f79d4a · outbound

This paper cites Remarks on multi-output gaussian process regression.Knowledge- Based Systems, 144:102–121, 2018.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Remarks on multi-output gaussian process regression.Knowledge- Based Systems, 144:102–121, 2018

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:35.277050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8e308a22-cc58-43fc-b53d-cb899bb195ec · outbound

This paper cites A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems.Journal of Computational Physics, 401:109020, 2020.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse pde problems.Journal of Computational Physics, 401:109020, 2020

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:35.004193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e439c542-bc68-4dee-b9e1-0591b3f88643 · outbound

This paper cites Multi-fidelity bayesian optimization via deep neural networks.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Multi-fidelity bayesian optimization via deep neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:34.659936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cb778e0c-c881-4a48-afe0-17f8e2004bd5 · outbound

This paper cites An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization.Structural and Multidisciplinary Optimization, 65(7):188, 2022.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization.Structural and Multidisciplinary Optimization, 65(7):188, 2022

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:34.383495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4a2d322a-4587-4d24-8dc5-2aed7087fbd9 · outbound

This paper cites Multi-fidelity optimization via surrogate modelling.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Multi-fidelity optimization via surrogate modelling

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:34.054794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 910d25b4-eaa2-4f82-8995-0cae5b579617 · outbound

This paper cites Multi-fidelity bayesian neural networks: Algorithms and applications.Journal of Computational Physics, 438:110361, 2021.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Multi-fidelity bayesian neural networks: Algorithms and applications.Journal of Computational Physics, 438:110361, 2021

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:33.809930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6cc7f2fd-ea0b-4570-be19-625e6ac1537f · outbound

This paper cites Hesthaven.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Hesthaven

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:33.538340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b79e61c3-4f8a-4e18-9dcb-5917b64a067c · outbound

This paper cites Taylan Turan, Jiaxiang Yi, and Miguel A.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Taylan Turan, Jiaxiang Yi, and Miguel A

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:33.281885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 20287c8a-29c4-4a52-884a-e3929693c8a2 · outbound

This paper cites Deshpande, Soumya Ghosh, Tin D.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Deshpande, Soumya Ghosh, Tin D

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:33.042952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48ca7018-6f01-40a5-aa80-8a89c12f2275 · outbound

This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:32.730645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 77c94bc9-950c-4bcd-af8b-1053ca86d244 · outbound

This paper cites How to evaluate uncertainty estimates in machine learning for regression?Neural Networks, 173:106203, May 2024.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling How to evaluate uncertainty estimates in machine learning for regression?Neural Networks, 173:106203, May 2024

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:32.426006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1dfd05dd-89c3-42e6-b4a6-bd4e39d25e4a · outbound

This paper cites Kingma and Jimmy Ba.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Kingma and Jimmy Ba

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:29.189455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1f1b6f5-55e4-4b10-9a46-84efc31564e5 · outbound

This paper cites Probabilistic machine learning: Advanced topics, 2022.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Probabilistic machine learning: Advanced topics, 2022

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:32.092280Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 752bfda7-5303-4afd-8993-02edb91deaba · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Bayesian learning via stochastic gradient langevin dynamics

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:29.470814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de05ac0e-c896-4106-9cd4-831350c192a5 · outbound

This paper cites An overview of gradient descent optimization algorithms.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling An overview of gradient descent optimization algorithms

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T16:38:29.595800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c96b7370-cb4f-4409-88c2-187db1043b30 · outbound

This paper cites Elastic properties of reinforced solids: some theoretical principles.Journal of the Mechanics and Physics of Solids, 11(5):357–372, 1963.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Elastic properties of reinforced solids: some theoretical principles.Journal of the Mechanics and Physics of Solids, 11(5):357–372, 1963

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:31.812502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0c71d0fb-ebe7-4b25-a7a9-b7187909248c · outbound

This paper cites Ferreira, F.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Ferreira, F

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:31.583880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 450c71fc-11d9-4024-b6d1-8b500190db87 · outbound

This paper cites Hands- on bayesian neural networks—a tutorial for deep learning users.IEEE Computational Intelligence Magazine, 17(2):29–48, 2022.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Hands- on bayesian neural networks—a tutorial for deep learning users.IEEE Computational Intelligence Magazine, 17(2):29–48, 2022

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:38:31.335567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e804563c-b863-4c3a-a5cc-e8bbd97e2a1c · outbound

This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:31.062900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b9a36927-60a4-4496-8440-daf7c377b3df · outbound

This paper cites an unresolved cited work.

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:38:30.823878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers