ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2109.01050
  4. Cited By
Characterizing possible failure modes in physics-informed neural
  networks
v1v2 (latest)

Characterizing possible failure modes in physics-informed neural networks

2 September 2021
Aditi S. Krishnapriyan
A. Gholami
Shandian Zhe
Robert M. Kirby
Michael W. Mahoney
    PINNAI4CE
ArXiv (abs)PDFHTML

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

50 / 267 papers shown
Combined space-time reduced-order model with 3D deep convolution for
  extrapolating fluid dynamics
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics
Indu Kant Deo
Rui Gao
R. Jaiman
AI4CE
105
0
0
01 Nov 2022
Neuro-symbolic partial differential equation solver
Neuro-symbolic partial differential equation solver
Pouria A. Mistani
Samira Pakravan
Rajesh Ilango
S. Choudhry
Frédéric Gibou
151
1
0
25 Oct 2022
JAX-DIPS: Neural bootstrapping of finite discretization methods and
  application to elliptic problems with discontinuities
JAX-DIPS: Neural bootstrapping of finite discretization methods and application to elliptic problems with discontinuitiesJournal of Computational Physics (JCP), 2022
Pouria A. Mistani
Samira Pakravan
Rajesh Ilango
Frédéric Gibou
253
13
0
25 Oct 2022
SeismicNet: Physics-informed neural networks for seismic wave modeling
  in semi-infinite domain
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domainComputer Physics Communications (CPC), 2022
Pu Ren
Chengping Rao
Su Chen
Jian-Xun Wang
Hao Sun
Yang Liu
444
66
0
25 Oct 2022
Less Emphasis on Difficult Layer Regions: Curriculum Learning for
  Singularly Perturbed Convection-Diffusion-Reaction Problems
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction ProblemsEast Asian Journal on Applied Mathematics (EAJAM), 2022
Yufeng Wang
Cong Xu
Min Yang
Jin Zhang
173
11
0
23 Oct 2022
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed
  Neural Networks
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural NetworksInternational Conference on Machine Learning (ICML), 2022
Shibo Li
Michael Penwarden
Yiming Xu
Conor Tillinghast
Akil Narayan
Robert M. Kirby
Shandian Zhe
AI4CE
226
6
0
23 Oct 2022
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural
  Networks on Coupled Ordinary Differential Equations
Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential EquationsAnnual Conference on Information Sciences and Systems (CISS), 2022
Alexander New
B. Eng
A. Timm
A. Gearhart
163
8
0
14 Oct 2022
PDEBENCH: An Extensive Benchmark for Scientific Machine Learning
PDEBENCH: An Extensive Benchmark for Scientific Machine LearningNeural Information Processing Systems (NeurIPS), 2022
M. Takamoto
T. Praditia
Raphael Leiteritz
Dan MacKinlay
Francesco Alesiani
Dirk Pflüger
Mathias Niepert
AI4CE
615
338
0
13 Oct 2022
A Unified Hard-Constraint Framework for Solving Geometrically Complex
  PDEs
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEsNeural Information Processing Systems (NeurIPS), 2022
Songming Liu
Zhongkai Hao
Chengyang Ying
Hang Su
Jun Zhu
Ze Cheng
AI4CE
286
27
0
06 Oct 2022
Homotopy-based training of NeuralODEs for accurate dynamics discovery
Homotopy-based training of NeuralODEs for accurate dynamics discoveryNeural Information Processing Systems (NeurIPS), 2022
Joon-Hyuk Ko
Hankyul Koh
Nojun Park
W. Jhe
372
11
0
04 Oct 2022
Random Weight Factorization Improves the Training of Continuous Neural
  Representations
Random Weight Factorization Improves the Training of Continuous Neural Representations
Sizhuang He
Hanwen Wang
Jacob H. Seidman
P. Perdikaris
263
17
0
03 Oct 2022
Failure-informed adaptive sampling for PINNs
Failure-informed adaptive sampling for PINNsSIAM Journal on Scientific Computing (SISC), 2022
Zhiwei Gao
Liang Yan
Tao Zhou
351
131
0
01 Oct 2022
Implicit Neural Spatial Representations for Time-dependent PDEs
Implicit Neural Spatial Representations for Time-dependent PDEsInternational Conference on Machine Learning (ICML), 2022
Honglin Chen
Rundi Wu
E. Grinspun
Changxi Zheng
Julius Berner
AI4CE
436
45
0
30 Sep 2022
Variationally Mimetic Operator Networks
Variationally Mimetic Operator NetworksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022
Dhruv V. Patel
Deep Ray
M. Abdelmalik
T. Hughes
Assad A. Oberai
325
33
0
26 Sep 2022
Deep Reinforcement Learning for Adaptive Mesh Refinement
Deep Reinforcement Learning for Adaptive Mesh RefinementJournal of Computational Physics (JCP), 2022
C. Foucart
A. Charous
Pierre FJ Lermusiaux
AI4CE
134
32
0
25 Sep 2022
A novel corrective-source term approach to modeling unknown physics in
  aluminum extraction process
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
Haakon Robinson
E. Lundby
Adil Rasheed
J. Gravdahl
152
6
0
22 Sep 2022
Approximating the full-field temperature evolution in 3D electronic
  systems from randomized "Minecraft" systems
Approximating the full-field temperature evolution in 3D electronic systems from randomized "Minecraft" systems
Monika Stipsitz
H. Sanchis-Alepuz
AI4CE
120
3
0
21 Sep 2022
Investigating and Mitigating Failure Modes in Physics-informed Neural
  Networks (PINNs)
Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs)Communications in Computational Physics (Commun. Comput. Phys.), 2022
S. Basir
PINNAI4CE
245
38
0
20 Sep 2022
Residual-Quantile Adjustment for Adaptive Training of Physics-informed
  Neural Network
Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network
Jiayue Han
Zhiqiang Cai
Zhiyou Wu
Xiang Zhou
250
10
0
09 Sep 2022
Solving Elliptic Problems with Singular Sources using Singularity
  Splitting Deep Ritz Method
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz MethodSIAM Journal on Scientific Computing (SISC), 2022
Tianhao Hu
Bangti Jin
Zhi Zhou
339
8
0
07 Sep 2022
NeuralSI: Structural Parameter Identification in Nonlinear Dynamical
  Systems
NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems
Xuyang Li
H. Bolandi
Talal Salem
N. Lajnef
Vishnu Boddeti
156
3
0
26 Aug 2022
Domain-aware Control-oriented Neural Models for Autonomous Underwater
  Vehicles
Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles
Wenceslao Shaw-Cortez
Soumya Vasisht
Aaron Tuor
Ján Drgoňa
D. Vrabie
AI4CE
145
1
0
15 Aug 2022
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE
  Solvers
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE SolversAAAI Conference on Artificial Intelligence (AAAI), 2022
Namgyu Kang
Byeonghyeon Lee
Youngjoon Hong
S. Yun
Eunbyung Park
PINNAI4CE
209
23
0
26 Jul 2022
Thermodynamics of learning physical phenomena
Thermodynamics of learning physical phenomenaArchives of Computational Methods in Engineering (ACME), 2022
Elías Cueto
Francisco Chinesta
AI4CE
321
29
0
26 Jul 2022
Physics-Informed Neural Networks for Shell Structures
Physics-Informed Neural Networks for Shell Structures
Jan-Hendrik Bastek
D. Kochmann
AI4CE
118
70
0
26 Jul 2022
A comprehensive study of non-adaptive and residual-based adaptive
  sampling for physics-informed neural networks
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022
Chen-Chun Wu
Min Zhu
Qinyan Tan
Yadhu Kartha
Lu Lu
270
548
0
21 Jul 2022
Learning differentiable solvers for systems with hard constraints
Learning differentiable solvers for systems with hard constraintsInternational Conference on Learning Representations (ICLR), 2022
Geoffrey Negiar
Michael W. Mahoney
Aditi S. Krishnapriyan
223
42
0
18 Jul 2022
Earthformer: Exploring Space-Time Transformers for Earth System
  Forecasting
Earthformer: Exploring Space-Time Transformers for Earth System ForecastingNeural Information Processing Systems (NeurIPS), 2022
Zhihan Gao
Xingjian Shi
Hao Wang
Yi Zhu
Yuyang Wang
Mu Li
Dit-Yan Yeung
AI4TS
316
250
0
12 Jul 2022
Adaptive Self-supervision Algorithms for Physics-informed Neural
  Networks
Adaptive Self-supervision Algorithms for Physics-informed Neural NetworksEuropean Conference on Artificial Intelligence (ECAI), 2022
Shashank Subramanian
Robert M. Kirby
Michael W. Mahoney
A. Gholami
218
41
0
08 Jul 2022
Evaluating Error Bound for Physics-Informed Neural Networks on Linear
  Dynamical Systems
Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems
Shuheng Liu
Xiyue Huang
P. Protopapas
PINN
162
6
0
03 Jul 2022
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed
  Neural Networks
Momentum Diminishes the Effect of Spectral Bias in Physics-Informed Neural Networks
G. Farhani
Alexander Kazachek
Boyu Wang
211
8
0
29 Jun 2022
Deep Random Vortex Method for Simulation and Inference of Navier-Stokes
  Equations
Deep Random Vortex Method for Simulation and Inference of Navier-Stokes EquationsThe Physics of Fluids (Phys. Fluids), 2022
Rui Zhang
Tailin Wu
Qi Meng
Yue Wang
Rongchan Zhu
Bingguang Chen
Zhi-Ming Ma
Tie-Yan Liu
274
16
0
20 Jun 2022
Explaining the physics of transfer learning a data-driven subgrid-scale
  closure to a different turbulent flow
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flowPNAS Nexus (PNAS Nexus), 2022
Adam Subel
Yifei Guan
Ashesh Chattopadhyay
Pedram Hassanzadeh
AI4CE
176
50
0
07 Jun 2022
Is $L^2$ Physics-Informed Loss Always Suitable for Training
  Physics-Informed Neural Network?
Is L2L^2L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?Neural Information Processing Systems (NeurIPS), 2022
Chuwei Wang
Shanda Li
Di He
Liwei Wang
AI4CEPINN
580
34
0
04 Jun 2022
Physical Activation Functions (PAFs): An Approach for More Efficient
  Induction of Physics into Physics-Informed Neural Networks (PINNs)
Physical Activation Functions (PAFs): An Approach for More Efficient Induction of Physics into Physics-Informed Neural Networks (PINNs)Neurocomputing (Neurocomputing), 2022
J. Abbasi
Paal Ostebo Andersen
PINNAI4CE
255
37
0
29 May 2022
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
Statistical Inference of Constrained Stochastic Optimization via Sketched Sequential Quadratic Programming
Sen Na
Michael W. Mahoney
430
11
0
27 May 2022
Transformer for Partial Differential Equations' Operator Learning
Transformer for Partial Differential Equations' Operator Learning
Zijie Li
Kazem Meidani
A. Farimani
364
254
0
26 May 2022
Accelerated Training of Physics-Informed Neural Networks (PINNs) using
  Meshless Discretizations
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless DiscretizationsNeural Information Processing Systems (NeurIPS), 2022
Ramansh Sharma
Varun Shankar
215
59
0
19 May 2022
Finite Element Method-enhanced Neural Network for Forward and Inverse
  Problems
Finite Element Method-enhanced Neural Network for Forward and Inverse ProblemsAdvanced Modeling and Simulation in Engineering Sciences (AMSES), 2022
R. Meethal
B. Obst
Mohamed Khalil
A. Ghantasala
A. Kodakkal
K. Bletzinger
R. Wüchner
AI4CE
111
53
0
17 May 2022
Loss Landscape Engineering via Data Regulation on PINNs
Loss Landscape Engineering via Data Regulation on PINNsSocial Science Research Network (SSRN), 2022
Vignesh Gopakumar
Stanislas Pamela
D. Samaddar
PINN
160
28
0
16 May 2022
AutoKE: An automatic knowledge embedding framework for scientific
  machine learning
AutoKE: An automatic knowledge embedding framework for scientific machine learningIEEE Transactions on Artificial Intelligence (IEEE TAI), 2022
Mengge Du
Yuntian Chen
Dongxiao Zhang
AI4CE
195
12
0
11 May 2022
Physics-informed neural networks for PDE-constrained optimization and
  control
Physics-informed neural networks for PDE-constrained optimization and control
Jostein Barry-Straume
A. Sarshar
Andrey A. Popov
Adrian Sandu
PINNAI4CE
247
19
0
06 May 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of
  physics-informed neural networks
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networksComputer Methods in Applied Mechanics and Engineering (CMAME), 2022
R. Mojgani
Maciej Balajewicz
Pedram Hassanzadeh
PINN
222
56
0
05 May 2022
Learning Green's functions associated with time-dependent partial
  differential equations
Learning Green's functions associated with time-dependent partial differential equationsJournal of machine learning research (JMLR), 2022
N. Boullé
Seick Kim
Tianyi Shi
Alex Townsend
AI4CE
307
31
0
27 Apr 2022
Competitive Physics Informed Networks
Competitive Physics Informed NetworksInternational Conference on Learning Representations (ICLR), 2022
Qi Zeng
Yash Kothari
Spencer H. Bryngelson
F. Schafer
PINN
216
24
0
23 Apr 2022
Improved Training of Physics-Informed Neural Networks with Model
  Ensembles
Improved Training of Physics-Informed Neural Networks with Model EnsemblesIEEE International Joint Conference on Neural Network (IJCNN), 2022
Katsiaryna Haitsiukevich
Alexander Ilin
PINN
395
35
0
11 Apr 2022
On the Role of Fixed Points of Dynamical Systems in Training
  Physics-Informed Neural Networks
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
271
25
0
25 Mar 2022
Investigating Compounding Prediction Errors in Learned Dynamics Models
Investigating Compounding Prediction Errors in Learned Dynamics Models
Nathan Lambert
K. Pister
Roberto Calandra
AI4CE
222
39
0
17 Mar 2022
Respecting causality is all you need for training physics-informed
  neural networks
Respecting causality is all you need for training physics-informed neural networks
Sizhuang He
Shyam Sankaran
P. Perdikaris
PINNCMLAI4CE
407
235
0
14 Mar 2022
Modeling the Shape of the Brain Connectome via Deep Neural Networks
Modeling the Shape of the Brain Connectome via Deep Neural NetworksInformation Processing in Medical Imaging (IPMI), 2022
Haocheng Dai
M. Bauer
P. T. Fletcher
S. Joshi
MedImDiffM
111
1
0
06 Mar 2022
Previous
123456
Next
Page 5 of 6