Characterizing possible failure modes in physics-informed neural networks

2 September 2021

Aditi S. Krishnapriyan

Papers citing "Characterizing possible failure modes in physics-informed neural networks"

40 / 90 papers shown

Title
Neural tangent kernel analysis of PINN for advection-diffusion equation M. Saadat B. Gjorgiev L. Das G. Sansavini 12 0 0 21 Nov 2022
A Deep Double Ritz Method (D $^2$ RM) for solving Partial Differential Equations using Neural Networks C. Uriarte David Pardo I. Muga J. Muñoz‐Matute 22 17 0 07 Nov 2022
Neuro-symbolic partial differential equation solver Pouria A. Mistani Samira Pakravan Rajesh Ilango S. Choudhry Frédéric Gibou 21 1 0 25 Oct 2022
JAX-DIPS: Neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuities Pouria A. Mistani Samira Pakravan Rajesh Ilango Frédéric Gibou 13 8 0 25 Oct 2022
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain Pu Ren Chengping Rao Su Chen Jian-Xun Wang Hao-Lun Sun Yang Liu 21 40 0 25 Oct 2022
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations Alexander New B. Eng A. Timm A. Gearhart 12 4 0 14 Oct 2022
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs Songming Liu Zhongkai Hao Chengyang Ying Hang Su Jun Zhu Ze Cheng AI4CE 8 17 0 06 Oct 2022
Homotopy-based training of NeuralODEs for accurate dynamics discovery Joon-Hyuk Ko Hankyul Koh Nojun Park W. Jhe 30 8 0 04 Oct 2022
Failure-informed adaptive sampling for PINNs Zhiwei Gao Liang Yan Tao Zhou 16 76 0 01 Oct 2022
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method Tianhao Hu Bangti Jin Zhi Zhou 21 6 0 07 Sep 2022
Thermodynamics of learning physical phenomena Elías Cueto Francisco Chinesta AI4CE 19 22 0 26 Jul 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 16 351 0 21 Jul 2022
Learning differentiable solvers for systems with hard constraints Geoffrey Negiar Michael W. Mahoney Aditi S. Krishnapriyan 16 28 0 18 Jul 2022
Earthformer: Exploring Space-Time Transformers for Earth System Forecasting Zhihan Gao Xingjian Shi Hao Wang Yi Zhu Yuyang Wang Mu Li Dit-Yan Yeung AI4TS 31 145 0 12 Jul 2022
Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems Shuheng Liu Xiyue Huang P. Protopapas PINN 11 5 0 03 Jul 2022
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks G. Farhani Alexander Kazachek Boyu Wang 19 6 0 29 Jun 2022
Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? Chuwei Wang Shanda Li Di He Liwei Wang AI4CE PINN 11 28 0 04 Jun 2022
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs) J. Abbasi Paal Ostebo Andersen PINN AI4CE 19 13 0 29 May 2022
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming Sen Na Michael W. Mahoney 13 7 0 27 May 2022
Transformer for Partial Differential Equations' Operator Learning Zijie Li Kazem Meidani A. Farimani 35 140 0 26 May 2022
Loss Landscape Engineering via Data Regulation on PINNs Vignesh Gopakumar Stanislas Pamela D. Samaddar PINN 30 16 0 16 May 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 16 14 0 06 May 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks R. Mojgani Maciej Balajewicz P. Hassanzadeh PINN 21 45 0 05 May 2022
Learning Green's functions associated with time-dependent partial differential equations N. Boullé Seick Kim Tianyi Shi Alex Townsend AI4CE 8 25 0 27 Apr 2022
Improved Training of Physics-Informed Neural Networks with Model Ensembles Katsiaryna Haitsiukevich Alexander Ilin PINN 22 23 0 11 Apr 2022
On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 31 17 0 25 Mar 2022
Investigating Compounding Prediction Errors in Learned Dynamics Models Nathan Lambert K. Pister Roberto Calandra AI4CE 14 27 0 17 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 12 14 0 24 Feb 2022
Learning continuous models for continuous physics Aditi S. Krishnapriyan A. Queiruga N. Benjamin Erichson Michael W. Mahoney AI4CE 18 32 0 17 Feb 2022
When Do Flat Minima Optimizers Work? Jean Kaddour Linqing Liu Ricardo M. A. Silva Matt J. Kusner ODL 6 58 0 01 Feb 2022
Numerical Approximation of Partial Differential Equations by a Variable Projection Method with Artificial Neural Networks S. Dong Jielin Yang 32 17 0 24 Jan 2022
Learning finite difference methods for reaction-diffusion type equations with FCNN Yongho Kim Yongho Choi 11 8 0 04 Jan 2022
Fast PDE-constrained optimization via self-supervised operator learning Sifan Wang Mohamed Aziz Bhouri P. Perdikaris 40 28 0 25 Oct 2021
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs Biswajit Khara Aditya Balu Ameya Joshi S. Sarkar C. Hegde A. Krishnamurthy Baskar Ganapathysubramanian 22 19 0 04 Oct 2021
Physics-Guided Deep Learning for Dynamical Systems: A Survey Rui Wang Rose Yu AI4CE PINN 18 62 0 02 Jul 2021
Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks Franz M. Rohrhofer S. Posch C. Gößnitzer Bernhard C. Geiger PINN 13 39 0 03 May 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 489 0 09 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sifan Wang Hanwen Wang P. Perdikaris 131 437 0 18 Dec 2020
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework O. Hennigh S. Narasimhan M. A. Nabian Akshay Subramaniam Kaustubh Tangsali M. Rietmann J. Ferrandis Wonmin Byeon Z. Fang S. Choudhry PINN AI4CE 91 125 0 14 Dec 2020
Lagrangian Neural Networks M. Cranmer S. Greydanus Stephan Hoyer Peter W. Battaglia D. Spergel S. Ho PINN 121 419 0 10 Mar 2020