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

Observation 7fe54a41-d3db-4d3b-b5af-6923ce427940 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:12.695873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ffcb19a5-fe22-4bd2-99f6-962ed0723ab1 · outbound

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

Resolution: verified exact; doi, observed 2026-08-06T23:28:02.861361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9c938738-cba9-48be-a581-6318316f4c61 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:12.504373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 798bea2c-d1d3-4f99-88dd-a11a7334698a · outbound

This paper cites Physics-informed machine learning,.

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

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:59.031390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65c57a8a-afe0-40a0-ba71-35c99075eadd · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:12.276147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5f5615bc-ed57-4850-a0c6-0f79291c6a71 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:11.710608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fb1b68f8-1016-48a0-9b49-c70e584bc131 · outbound

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

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T23:28:11.467719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e9904be3-bc7d-42ad-888b-27f3d6bac5c0 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:11.185401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b2b406b8-5116-4717-9786-14c12d8feae0 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:10.921445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation beda0fbc-e154-4d10-8b76-d482ace23768 · outbound

This paper cites an unresolved cited work.

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

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:10.715250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 62780ed6-489c-446a-80c4-41286291d261 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:10.508037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e714a0a2-6da8-4d8f-8f89-f562b3903d17 · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T23:27:59.496855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 21a4dc65-04a4-414d-bf37-6656cc47df12 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:10.256665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 00512ec5-6af8-4dbd-97b1-275135ebe094 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:09.991043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fda59949-89f1-46cc-95b3-629f0eb987a6 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:09.791972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3f1fd96f-930b-470a-bc4a-557f894497ea · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1b7f6cf5-27fa-4aaa-8ad8-572a60beb980 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1ef3f8a5-c0da-4006-869e-640cfbdfc5e4 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:09.123459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fdcb70ef-3edd-459e-ab1e-686af0304d04 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bd749812-86ae-499b-9d72-115e797398e3 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:00.126647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26ef1e19-a809-45e8-8a9d-46b921245f02 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:08.271623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3a5c5f91-0bd0-4a21-81f4-3340234c9656 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.865502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4dfb8cd-cabc-471f-9a4f-fb56bbe98e88 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 66f18452-9c01-44f6-a8bf-97057c46e4bd · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.484753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b3ae491f-02bc-42a8-b6d4-a96b3e87c9a9 · outbound

This paper cites an unresolved cited work.

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

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:07.246267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d2a05667-7672-4ec7-a2f4-ad2b98777a2d · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:00.741644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b690b643-a6b5-4310-b678-62f80e426e0c · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:07.083361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b8441883-3448-4ea4-82e7-6037619c149e · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.856430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 064c91a3-e975-4d4a-9fd6-c761734fb5d6 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.672383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d820739d-e4c3-4a76-9613-6c6be94f5167 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.444738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b397eb26-e2ed-4b96-a3cb-0ac6a8755391 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:06.176208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cc638377-ff23-4eec-8fdf-b432651e9a1b · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:05.971990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16548f92-5086-4fb0-b2f6-e8e277c46e42 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:05.729137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 63d2622a-35f7-4038-96ca-8a1ff0d1de98 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9e3b9dc4-3aef-42fe-a29d-9f7accead1c2 · outbound

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

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T23:28:03.406706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 802c2ea2-f740-482e-8b02-4562b4ad1c3a · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:05.249322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 42d995ce-5e98-461a-b205-d8ff7b56f015 · outbound

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

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:28:04.951620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d898c7b7-6a04-4d7e-8b9e-e7c47fd02aaa · outbound

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

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:01.906465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddf93349-b07d-41d5-a007-677cc7c6c336 · outbound

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

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:28:03.156508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 19409f81-c334-422f-a7df-1ee4526cb60e · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 67067ba0-f912-4723-be73-d942c30b15f1 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d02ea97f-7c87-46c8-8b4f-e3f85593ab14 · outbound

This paper cites an unresolved cited work.

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

Reference 45

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:04.011895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 20aa0f9a-8dd7-42ee-83c1-947da89e3c85 · outbound

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

Resolution: unresolved; raw_fallback, observed 2026-08-06T23:28:03.721093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers