B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data

13 March 2020

Papers citing "B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data"

26 / 76 papers shown

Title
Calibrating constitutive models with full-field data via physics informed neural networks Craig M. Hamel K. Long S. Kramer AI4CE 27 28 0 30 Mar 2022
Error estimates for physics informed neural networks approximating the Navier-Stokes equations Tim De Ryck Ameya Dilip Jagtap S. Mishra PINN 25 115 0 17 Mar 2022
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors Yibo Yang Georgios Kissas P. Perdikaris BDL UQCV 22 40 0 06 Mar 2022
Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning Said Ouala Steven L. Brunton A. Pascual Bertrand Chapron F. Collard L. Gaultier Ronan Fablet PINN AI4TS AI4CE 18 10 0 11 Feb 2022
Physics-informed neural networks for solving parametric magnetostatic problems Andrés Beltrán-Pulido Ilias Bilionis D. Aliprantis 24 34 0 08 Feb 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 24 1,177 0 14 Jan 2022
De Rham compatible Deep Neural Network FEM M. Longo J. Opschoor Nico Disch Christoph Schwab Jakob Zech 17 8 0 14 Jan 2022
Solving time dependent Fokker-Planck equations via temporal normalizing flow Xiaodong Feng Li Zeng Tao Zhou AI4CE 31 25 0 28 Dec 2021
DeepParticle: learning invariant measure by a deep neural network minimizing Wasserstein distance on data generated from an interacting particle method Zhongjian Wang Jack Xin Zhiwen Zhang 39 15 0 02 Nov 2021
An extended physics informed neural network for preliminary analysis of parametric optimal control problems N. Demo M. Strazzullo G. Rozza PINN 28 33 0 26 Oct 2021
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yihang Gao Michael K. Ng 27 28 0 30 Aug 2021
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 45 209 0 16 Jul 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 39 64 0 02 Jul 2021
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks S. Maddu D. Sturm Christian L. Müller I. Sbalzarini AI4CE 18 80 0 02 Jul 2021
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs Tim De Ryck Siddhartha Mishra PINN 8 100 0 28 Jun 2021
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby 10 42 0 25 Jun 2021
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction Zhiqiang Gong Weien Zhou Jun Zhang Wei Peng W. Yao AI4CE 29 11 0 22 Jun 2021
Learning Functional Priors and Posteriors from Data and Physics Xuhui Meng Liu Yang Zhiping Mao J. Ferrandis George Karniadakis AI4CE 27 61 0 08 Jun 2021
Applying physics-based loss functions to neural networks for improved generalizability in mechanics problems Samuel J. Raymond David B. Camarillo PINN AI4CE 25 12 0 30 Apr 2021
Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi Alexandros Kalousis DRL AI4CE 22 54 0 25 Feb 2021
Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging Enrui Zhang Minglang Yin George Karniadakis 12 64 0 02 Sep 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sifan Wang Xinling Yu P. Perdikaris 30 874 0 28 Jul 2020
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs Siddhartha Mishra Roberto Molinaro PINN 15 170 0 29 Jun 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao-Lun Sun Yang Liu PINN AI4CE 15 222 0 10 Jun 2020
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization Rishikesh Ranade C. Hill Jay Pathak AI4CE 46 123 0 17 May 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,136 0 06 Jun 2015