Communities
Connect sessions
AI calendar
Organizations
Join Slack
Contact Sales

Terms and Conditions

Twitter GitHub LinkedIn Bluesky Youtube

© 2026 ResearchTrend.AI, All rights reserved.

Home
Papers
2108.04085
Cited By

Bayesian Deep Learning for Partial Differential Equation Parameter
Discovery with Sparse and Noisy Data

v1v2v3 (latest)

Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data

5 August 2021

Christophe Bonneville

Christopher Earls

ArXiv (abs)PDF HTML Github (7★)

Papers citing "Bayesian Deep Learning for Partial Differential Equation Parameter Discovery with Sparse and Noisy Data"

9 / 9 papers shown

A Comprehensive Review of Latent Space Dynamics Identification
Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling

Christophe Bonneville

Jun Sur Richard Park

William D. Fries

...

Debojyoti Ghosh

Jiun-Shyan Chen

340

27

0

16 Mar 2024

Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification
for Fast Physical Simulations

Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations

Christophe Bonneville

Debojyoti Ghosh

176

3

0

02 Dec 2023

Physics-constrained robust learning of open-form partial differential
equations from limited and noisy data

Physics-constrained robust learning of open-form partial differential equations from limited and noisy dataThe Physics of Fluids (Phys. Fluids), 2023

Dong-juan Zhang

422

16

0

14 Sep 2023

Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From
Noisy, Limited Data

Weak-PDE-LEARN: A Weak Form Based Approach to Discovering PDEs From Noisy, Limited DataJournal of Computational Physics (JCP), 2023

Christopher Earls

269

15

0

09 Sep 2023

Bayesian Reasoning for Physics Informed Neural Networks

Bayesian Reasoning for Physics Informed Neural Networks

Kornel Witkowski

320

0

0

25 Aug 2023

GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics
Identification through Deep Autoencoder

GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep AutoencoderComputer Methods in Applied Mechanics and Engineering (CMAME), 2023

Christophe Bonneville

Debojyoti Ghosh

220

41

0

10 Aug 2023

Discovering stochastic partial differential equations from limited data
using variational Bayes inference

Discovering stochastic partial differential equations from limited data using variational Bayes inferenceComputer Methods in Applied Mechanics and Engineering (CMAME), 2023

Yogesh Chandrakant Mathpati

241

15

0

28 Jun 2023

PDE-LEARN: Using Deep Learning to Discover Partial Differential
Equations from Noisy, Limited Data

PDE-LEARN: Using Deep Learning to Discover Partial Differential Equations from Noisy, Limited DataNeural Networks (NN), 2022

Christopher Earls

298

30

0

09 Dec 2022

PDE-READ: Human-readable Partial Differential Equation Discovery using
Deep Learning

PDE-READ: Human-readable Partial Differential Equation Discovery using Deep LearningNeural Networks (NN), 2021

Christopher Earls

437

39

0

01 Nov 2021

Page 1 of 1