Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

21 September 2018

Papers citing "Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems"

50 / 56 papers shown

Title
Learning Specialized Activation Functions for Physics-informed Neural Networks Honghui Wang Lu Lu Shiji Song Gao Huang PINN AI4CE 16 11 0 08 Aug 2023
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations Mattia Silvestri Federico Baldo Eleonora Misino M. Lombardi AI4CE 16 1 0 17 Jun 2023
A Bi-fidelity DeepONet Approach for Modeling Uncertain and Degrading Hysteretic Systems Subhayan De P. Brewick 31 0 0 25 Apr 2023
Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook Xuan Di Rongye Shi Zhaobin Mo Yongjie Fu PINN AI4TS AI4CE 26 28 0 03 Mar 2023
h-analysis and data-parallel physics-informed neural networks Paul Escapil-Inchauspé G. A. Ruz PINN AI4CE 27 2 0 17 Feb 2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification A. Alberts Ilias Bilionis 32 12 0 18 Jan 2023
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks Olga Graf P. Flores P. Protopapas K. Pichara PINN 32 6 0 14 Dec 2022
Interpretability and accessibility of machine learning in selected food processing, agriculture and health applications N. Ranasinghe A. Ramanan S. Fernando P. N. Hameed D. Herath T. Malepathirana P. Suganthan M. Niranjan Saman K. Halgamuge 8 2 0 30 Nov 2022
Physics-Informed Koopman Network Yuying Liu A. Sholokhov Hassan Mansour S. Nabi AI4CE 25 3 0 17 Nov 2022
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition Yihang Gao Ka Chun Cheung Michael K. Ng 25 15 0 16 Nov 2022
Physics-informed neural networks for operator equations with stochastic data Paul Escapil-Inchauspé G. A. Ruz 29 2 0 15 Nov 2022
Partial Differential Equations Meet Deep Neural Networks: A Survey Shudong Huang Wentao Feng Chenwei Tang Jiancheng Lv AI4CE AIMat 24 17 0 27 Oct 2022
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency Weilong Guan Kai-Ping Yang Yinsheng Chen Zhong Guan PINN AI4CE 18 12 0 19 Oct 2022
Variationally Mimetic Operator Networks Dhruv V. Patel Deep Ray M. Abdelmalik T. Hughes Assad A. Oberai 49 23 0 26 Sep 2022
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators Zongren Zou Xuhui Meng Apostolos F. Psaros George Karniadakis AI4CE 25 36 0 25 Aug 2022
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks Chen-Chun Wu M. Zhu Qinyan Tan Yadhu Kartha Lu Lu 24 352 0 21 Jul 2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations Junho Choi N. Kim Youngjoon Hong AI4CE 24 0 0 21 Jul 2022
Neural and spectral operator surrogates: unified construction and expression rate bounds L. Herrmann Christoph Schwab Jakob Zech 45 9 0 11 Jul 2022
Scalable algorithms for physics-informed neural and graph networks K. Shukla Mengjia Xu N. Trask George Karniadakis PINN AI4CE 69 40 0 16 May 2022
Bayesian Physics-Informed Extreme Learning Machine for Forward and Inverse PDE Problems with Noisy Data Xu Liu Wenjuan Yao Wei Peng Weien Zhou PINN AI4CE 43 25 0 14 May 2022
Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics Xin-Yang Liu Min Zhu Lu Lu Hao Sun Jian-Xun Wang PINN AI4CE 28 45 0 09 May 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 29 14 0 06 May 2022
A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic Barbara Z Cunha C. Droz A. Zine Stéphane Foulard M. Ichchou AI4CE 29 84 0 13 Apr 2022
Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets Subhayan De Matthew J. Reynolds M. Hassanaly Ryan N. King Alireza Doostan AI4CE 26 37 0 03 Apr 2022
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations Weiheng Zhong Hadi Meidani DRL 16 36 0 21 Mar 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Z. Wang Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 24 14 0 24 Feb 2022
Learning Stochastic Dynamics with Statistics-Informed Neural Network Yuanran Zhu Yunhao Tang Changho Kim 19 18 0 24 Feb 2022
MIONet: Learning multiple-input operators via tensor product Pengzhan Jin Shuai Meng Lu Lu 20 155 0 12 Feb 2022
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty Paz Fink Shustin Shashanka Ubaru Vasileios Kalantzis L. Horesh H. Avron 21 2 0 10 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 26 1,179 0 14 Jan 2022
Learning To Estimate Regions Of Attraction Of Autonomous Dynamical Systems Using Physics-Informed Neural Networks Cody Scharzenberger Joe Hays 37 3 0 18 Nov 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems Jeremy Yu Lu Lu Xuhui Meng George Karniadakis PINN AI4CE 24 449 0 01 Nov 2021
Learning Density Distribution of Reachable States for Autonomous Systems Yue Meng Dawei Sun Zeng Qiu Md Tawhid Bin Waez Chuchu Fan 77 19 0 14 Sep 2021
Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yihang Gao Michael K. Ng 38 28 0 30 Aug 2021
Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models Ling Guo Hao Wu Tao Zhou AI4CE 6 45 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
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs) Michael Penwarden Shandian Zhe A. Narayan Robert M. Kirby 23 42 0 25 Jun 2021
Neural network architectures using min-plus algebra for solving certain high dimensional optimal control problems and Hamilton-Jacobi PDEs Jérome Darbon P. Dower Tingwei Meng 8 22 0 07 May 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 66 17 0 23 Apr 2021
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity J. Fuhg N. Bouklas PINN AI4CE 25 90 0 15 Apr 2021
Bayesian neural networks for weak solution of PDEs with uncertainty quantification Xiaoxuan Zhang K. Garikipati AI4CE 46 11 0 13 Jan 2021
An overview on deep learning-based approximation methods for partial differential equations C. Beck Martin Hutzenthaler Arnulf Jentzen Benno Kuckuck 30 146 0 22 Dec 2020
Physics-Informed Neural Network for Modelling the Thermochemical Curing Process of Composite-Tool Systems During Manufacture S. Niaki E. Haghighat Trevor Campbell Xinglong Li R. Vaziri AI4CE 13 203 0 27 Nov 2020
Physics-informed Neural-Network Software for Molecular Dynamics Applications Taufeq Mohammed Razakh Beibei Wang Shane Jackson R. Kalia A. Nakano K. Nomura P. Vashishta PINN 13 11 0 06 Nov 2020
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder Youngkyu Kim Youngsoo Choi David Widemann T. Zohdi AI4CE 9 188 0 25 Sep 2020
Physics informed deep learning for computational elastodynamics without labeled data Chengping Rao Hao-Lun Sun Yang Liu PINN AI4CE 17 222 0 10 Jun 2020
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations Christopher J. Arthurs A. King PINN 35 51 0 02 May 2020
SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems Pengzhan Jin Zhen Zhang Aiqing Zhu Yifa Tang George Karniadakis 16 21 0 11 Jan 2020
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs Liu Yang Sean Treichler Thorsten Kurth Keno Fischer D. Barajas-Solano ... Valentin Churavy A. Tartakovsky Michael Houston P. Prabhat George Karniadakis AI4CE 41 37 0 29 Oct 2019
Deep Learning of Subsurface Flow via Theory-guided Neural Network Nanzhe Wang Dongxiao Zhang Haibin Chang Heng Li AI4CE 25 226 0 24 Oct 2019