Paper List of Physics-Informed Neural Network (PINN)
- Continues to update based on [PINNpapers] contributed by IDRL lab.
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Torres, Edgar, and Mathias Niepert. "Survey: Adaptive Physics-informed Neural Networks." Neurips 2024 Workshop Foundation Models for Science: Progress, Opportunities, and Challenges. [Paper]
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PINNs for Medical Image Analysis: A Survey, Chayan Banerjee, Kien Nguyen, Olivier Salvado, Truyen Tran, Clinton Fookes [Paper]
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[arXiv:2410.13228] From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning, Juan Diego Toscano, Vivek Oommen, Alan John Varghese, Zongren Zou, Nazanin Ahmadi Daryakenari, Chenxi Wu, George Em Karniadakis [Paper]
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Physics-Informed Computer Vision: A Review and Perspectives, [Paper]
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Scientific Machine Learning Through Physics–Informed Neural Networks: Where We Are and What’s Next? Journal of Scientific Computing (2022) 92:88, [Paper]
- Raissi, Maziar, Alireza Yazdani, and George Em Karniadakis. "Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations." Science 367.6481 (2020): 1026-1030. [Paper]
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Introduction to Physics Informed Neural Networks | A hands on, project based course, [Youtube]
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Physics Informed Neural Networks (PINNs) || Ordinary Differential Equations || Step-by-Step Tutorial, [Youtube]
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Physics Informed Machine Learning: High Level Overview of AI and ML in Science and Engineering, [Youtube]
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Physics Informed Neural Networks, [Youtube]
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Learning Physics Informed Machine Learning Part 1- Physics Informed Neural Networks (PINNs) [Youtube]
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Learning Physics Informed Machine Learning Part 2- Inverse Physics Informed Neural Networks (PINNs) [Youtube]
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Learning Physics Informed Machine Learning Part 3- Physics Informed DeepONets [Youtube]
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Physics Informed Neural Networks for Soft Matter Problems (Paper Review) [Youtube]
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Physics-Informed Machine Learning (PIML) and Kolmogorov-Arnold Networks (KANs)- Caltech's CMX 2025 [Youtube]
---------------------- ETH Zürich AI in the Sciences and Engineering 2024 ----------------------
- ETH Zürich AISE: Physics-Informed Neural Networks – Introduction [Youtube]
- ETH Zürich AISE: Physics-Informed Neural Networks – Limitations and Extensions Part 1 [Youtube]
- ETH Zürich AISE: Physics-Informed Neural Networks – Limitations and Extensions Part 2 [Youtube]
- ETH Zürich AISE: Physics-Informed Neural Networks – Theory Part 1 [Youtube]
- ETH Zürich AISE: Physics-Informed Neural Networks – Theory Part 2 [Youtube]
- ETH Zürich AISE: Importance of PDEs in Science [Youtube]
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[Project page] [Physics Informed Deep Learning---Data-driven solutions and discovery of Nonlinear Partial Differential Equations]
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[arXiv:1711.10561] Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations, Maziar Raissi, Paris Perdikaris, George Em Karniadakis [Paper]
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[arXiv:1711.10566] Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations, Maziar Raissi, Paris Perdikaris, George Em Karniadakis [Paper]
- DeepXDE: A Deep Learning Library for Solving Differential Equations, Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis, SIAM Review, 2021. [paper][code]
- NVIDIA SimNet™: An AI-Accelerated Multi-Physics Simulation Framework, Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam, Kaustubh Tangsali, Zhiwei Fang, Max Rietmann, Wonmin Byeon, Sanjay Choudhry, ICCS, 2021. [paper]
- SciANN: A Keras wrapper for scientific computations and physics-informed deep learning using artificial neural networks, Ehsan Haghighat, Ruben Juanes, arXiv preprint arXiv:2005.08803, 2020. [paper][code]
- Elvet -- a neural network-based differential equation and variational problem solver, Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky, arXiv:2103.14575 [hep-lat, physics:hep-ph, physics:hep-th, stat], 2021. [paper][code]
- TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks, Levi D. McClenny, Mulugeta A. Haile, Ulisses M. Braga-Neto, arXiv:2103.16034 [physics], 2021. [paper][code]
- PyDEns: a Python Framework for Solving Differential Equations with Neural Networks, Alex Koryagin, er, Roman Khudorozkov, Sergey Tsimfer, arXiv:1909.11544 [cs, stat], 2019. [paper]
- NeuroDiffEq: A Python package for solving differential equations with neural networks, Feiyu Chen, David Sondak, Pavlos Protopapas, Marios Mattheakis, Shuheng Liu, Devansh Agarwal, Marco Di Giovanni, Journal of Open Source Software, 2020. [paper][code]
- Universal Differential Equations for Scientific Machine Learning, Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, Kirill Zubov, Rohit Supekar, Dominic Skinner, Ali Ramadhan, Alan Edelman, arXiv:2001.04385 [cs, math, q-bio, stat], 2020. [paper][code]
- NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations, Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, N Vinchhi, , Kaushik Balakrishnan, Devesh Upadhyay, Chris Rackauckas, arXiv:2107.09443 [cs], 2021. [paper][code]
- IDRLnet: A Physics-Informed Neural Network Library, Wei Peng, Jun Zhang, Weien Zhou, Xiaoyu Zhao, Wen Yao, Xiaoqian Chen, arXiv:2107.04320 [cs, math], 2021. [paper][code]
- NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators, Zongren Zou, Xuhui Meng, Apostolos F. Psaros, George Em Karniadakis, UNKNOWN_JOURNAL, 2022. [paper][code]
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PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems, Bocheng Zeng, Qi Wang, Mengtao Yan, Yang Liu, Ruizhi Chengze, Yi Zhang, Hongsheng Liu, Zidong Wang, Hao Sun [Paper]
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Physics-Informed Neuro-Evolution (PINE): A Survey and Prospects, Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi, Pao-Hsiung Chiu, Jiao Liu, Yew-Soon Ong [Paper]
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CryoGEM: Physics-Informed Generative Cryo-Electron Microscopy [Paper]
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Physics-Informed Variational State-Space Gaussian Processes [Paper]
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Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators, [Paper]
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Physics-Regularized Multi-Modal Image Assimilation for Brain Tumor Localization, [Paper]
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Dual Cone Gradient Descent for Training Physics-Informed Neural Networks, Youngsik Hwang, Dong-Young Lim [Paper]
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Musgrave, Jonathan, and Shu-Wei Huang. "Fourier Domain Physics Informed Neural Network." arXiv preprint arXiv:2409.19895 (2024). [Paper]
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Cooley, Madison, et al. "Fourier PINNs: From Strong Boundary Conditions to Adaptive Fourier Bases." arXiv preprint arXiv:2410.03496 (2024). [Paper]
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Song, Yuchen, et al. "SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning." Communications Engineering 3.1 (2024): 109. [Paper]
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Robust Variational Physics-Informed Neural Networks, [Paper]
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[NeurIPS 2023] Separable Physics-Informed Neural Networks, [Paper] [Code]
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Kamtue, Kawisorn, et al. "PhyOT: Physics-informed object tracking in surveillance cameras." ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024. [Paper]
- Borate, Prabhav, et al. "Using a physics-informed neural network and fault zone acoustic monitoring to predict lab earthquakes." Nature communications 14.1 (2023): 3693. [Paper]
- -PINNs: physics-informed neural networks on complex geometries, [Paper]
- Physics-informed deep learning for incompressible laminar flows, Chengping Rao, Hao Sun, Yang Liu, Theoretical and Applied Mechanics Letters, 2020 [Paper]
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, M. Raissi, P. Perdikaris, G. E. Karniadakis, Journal of Computational Physics, 2019. [paper]
- The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems, E Weinan, Bing Yu, Communications in Mathematics and Statistics, 2018. [paper]
- DGM: A deep learning algorithm for solving partial differential equations, Justin Sirignano, Konstantinos Spiliopoulos, Journal of Computational Physics, 2018. [paper]
- SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs, Amuthan A. Ramabathiran, Ramach, Prabhu ran, Journal of Computational Physics, 2021. [paper][code]
- Deep neural network methods for solving forward and inverse problems of time fractional diffusion equations with conformable derivative, Yinlin Ye, Yajing Li, Hongtao Fan, Xinyi Liu, Hongbing Zhang, arXiv:2108.07490 [cs, math], 2021. [paper]
- NH-PINN: Neural homogenization based physics-informed neural network for multiscale problems, Wing Tat Leung, Guang Lin, Zecheng Zhang, arXiv:2108.12942 [cs, math], 2021. [paper]
- Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning, Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark, arXiv:2109.13901 [physics], 2021. [paper]
- Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method, Yuntian Chen, Dou Huang, Dongxiao Zhang, Junsheng Zeng, Nanzhe Wang, Haoran Zhang, Jinyue Yan, Journal of Computational Physics, 2021. [paper]
- Learning in Sinusoidal Spaces with Physics-Informed Neural Networks, Jian Cheng Wong, Chinchun Ooi, Abhishek Gupta, Yew-Soon Ong, arXiv:2109.09338 [physics], 2021. [paper]
- HyperPINN: Learning parameterized differential equations with physics-informed hypernetworks, Filipe de Avila Belbute-Peres, Yi-fan Chen, Fei Sha, NIPS, 2021. [paper]
- Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries, AliKashefi, TapanMukerji, Journal of Computational Physics, 2022. [paper]
- Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems, HanGao, Matthew J.Zahr, Jian-XunWang, Computer Methods in Applied Mechanics and Engineering, 2022. [paper]
- PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network, Longxiang Jiang, Liyuan Wang, Xinkun Chu, Yonghao Xiao and Hao Zhang, arXiv:2208.04319 [cs.NE], 2022. [paper]
- ModalPINN : an extension of Physics-Informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors, * Ga´etan Raynaud , S´ebastien Houde, Fr´ed´erick P Gosselin*, Journal of Computational Physics, 2022. [paper]
- ∆-PINNs: physics-informed neural networks on complex geometries, Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris, Arxiv, 2022. [paper]
- Robust Regression with Highly Corrupted Data via Physics Informed Neural Networks, Wei Peng, Wen Yao, Weien Zhou, Xiaoya Zhang, Weijie Yao, ArXiv, 2022. [paper][code]
- Parallel Physics-Informed Neural Networks via Domain Decomposition, Khemraj Shukla, Ameya D. Jagtap, George Em Karniadakis, arXiv:2104.10013 [cs], 2021. [paper]
- Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations, Ben Moseley, Andrew Markham, Tarje Nissen-Meyer, arXiv:2107.07871 [physics], 2021. [paper]
- PPINN: Parareal physics-informed neural network for time-dependent PDEs, Xuhui Meng, Zhen Li, Dongkun Zhang, George Em Karniadakis, Computer Methods in Applied Mechanics and Engineering, 2020. [paper]
- When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?, Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi, arXiv:2109.09444 [cs, math, stat], 2021. [paper]
- Scaling physics-informed neural networks to large domains by using domain decomposition, Ben Moseley, Andrew Markham, Tarje Nissen-Meyer, NIPS, 2021. [paper]
- Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations, Ben Moseley, Andrew Markham, Tarje Nissen-Meyer, arXiv:2107.07871 [physics], 2021. [paper]
- Improved Deep Neural Networks with Domain Decomposition in Solving Partial Differential Equations, Wei Wu, Xinlong Feng, Hui Xu, Journal of Scientific Computing, 2022. [paper]
- INN: Interfaced neural networks as an accessible meshless approach for solving interface PDE problems, Sidi Wu, Benzhuo Lu, Journal of Computational Physics, 2022. [paper][code]
- Self-adaptive loss balanced Physics-informed neural networks for the incompressible Navier-Stokes equations, Zixue Xiang, Wei Peng, Xiaohu Zheng, Xiaoyu Zhao, Wen Yao, arXiv:2104.06217 [physics], 2021. [paper]
- A Dual-Dimer method for training physics-constrained neural networks with minimax architecture, Dehao Liu, Yan Wang, Neural Networks, 2021. [paper]
- Adversarial Multi-task Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations, Pongpisit Thanasutives, Masayuki Numao, Ken-ichi Fukui, arXiv:2104.14320 [cs, math], 2021. [paper]
- DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation, Jungeun Kim, Kookjin Lee, Dongeun Lee, Sheo Yon Jin, Noseong Park, AAAI, 2021. [paper]
- Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems, Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis, Arxiv, 2021. [paper]
- CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method, Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, Yew-Soon Ong, Arxiv, 2021. [paper]
- A hybrid physics-informed neural network for nonlinear partial differential equation, Chunyue Lv, Lei Wang, Chenming Xie, Arxiv, 2021. [paper]
- Multi-Objective Loss Balancing for Physics-Informed Deep Learning, Rafael Bischof, Michael Kraus, Arxiv, 2021. [paper]
- A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network, Zhiwei Fang, IEEE Transactions on Neural Networks and Learning Systems, 2021. [paper]
- RPINNs: Rectified-physics informed neural networks for solving stationary partial differential equations, Pai Peng, Jiangong Pan, Hui Xu, Xinlong Feng, Computers & Fluids, 2022. [paper]
- A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks, Chenxi Wu,Min Zhu,Qinyang Tan,Yadhu Kartha,Lu Lu, arXiv:2207.10289 [cs], 2022. [paper]
- A Novel Adaptive Causal Sampling Method for Physics-Informed Neural Networks, Jia Guo, Haifeng Wang, Chenping Hou, arXiv:2210.12914 [cs], 2022. [paper]
- Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations, Ramansh Sharma, Varun Shankar, NeurIPS, 2022. [paper]
- Is L2 Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network, Chuwei Wang, Shanda Li, Di He, Liwei Wang, NeurIPS, 2022. [paper]
- A physics-aware learning architecture with input transfer networks for predictive modeling, Amir Behjat, Chen Zeng, Rahul Rai, Ion Matei, David Doermann, Souma Chowdhury, Applied Soft Computing, 2020. [paper]
- Transfer learning based multi-fidelity physics informed deep neural network, Souvik Chakraborty, Journal of Computational Physics, 2021. [paper]
- Transfer learning enhanced physics informed neural network for phase-field modeling of fracture, Somdatta Goswami, Cosmin Anitescu, Souvik Chakraborty, Timon Rabczuk, Theoretical and Applied Fracture Mechanics, 2020. [paper]
- Meta-learning PINN loss functions, Apostolos F. Psaros, Kenji Kawaguchi, George Em Karniadakis, arXiv:2107.05544 [cs], 2021. [paper]
- Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh, Tian Qin,Alex Beatson,Deniz Oktay,Nick McGreivy,Ryan P. Adams, arXiv:2211.01604 [cs], 2022. [paper]
- Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach, Michael Penwarden, Sh Zhe, ian, Akil Narayan, Robert M. Kirby, Arxiv, 2021. [paper]
- A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime, Constantin Grigo, Phaedon-Stelios Koutsourelakis, Journal of Computational Physics, 2019. [paper]
- Adversarial uncertainty quantification in physics-informed neural networks, Yibo Yang, Paris Perdikaris, Journal of Computational Physics, 2019. [paper]
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data, Liu Yang, Xuhui Meng, George Em Karniadakis, Journal of Computational Physics, 2021. [paper]
- PID-GAN: A GAN Framework based on a Physics-informed Discriminator for Uncertainty Quantification with Physics, Arka Daw, M. Maruf, Anuj Karpatne, arXiv:2106.02993 [cs, stat], 2021. [paper]
- Quantifying Uncertainty in Physics-Informed Variational Autoencoders for Anomaly Detection, Marcus J. Neuer, ESTEP, 2020. [paper]
- A Physics-Data-Driven Bayesian Method for Heat Conduction Problems, Xinchao Jiang, Hu Wang, Yu li, arXiv:2109.00996 [cs, math], 2021. [paper]
- Wasserstein Generative Adversarial Uncertainty Quantification in Physics-Informed Neural Networks, Yihang Gao, Michael K. Ng, arXiv:2108.13054 [cs, math], 2021. [paper]
- Flow Field Tomography with Uncertainty Quantification using a Bayesian Physics-Informed Neural Network, Joseph P. Molnar, Samuel J. Grauer, arXiv:2108.09247 [physics], 2021. [paper]
- Stochastic Physics-Informed Neural Networks (SPINN): A Moment-Matching Framework for Learning Hidden Physics within Stochastic Differential Equations, Jared O'Leary, Joel A. Paulson, Ali Mesbah, arXiv:2109.01621 [cs], 2021. [paper]
- Spectral PINNs: Fast Uncertainty Propagation with Physics-Informed Neural Networks, Björn Lütjens, Catherine H. Crawford, Mark Veillette, Dava Newman, NIPS, 2021. [paper]
- Robust Learning of Physics Informed Neural Networks, Ch Bajaj, rajit, Luke McLennan, Timothy Andeen, Avik Roy, Arxiv, 2021. [paper]
- Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems, Kevin Linka, Amelie Schäfer, Xuhui Meng, Zongren Zou, George EmKarniadakis, Ellen Kuhl, Computer Methods in Applied Mechanics and Engineering, 2022. [paper]
- Multi-output physics-informed neural networks for forward and inverse PDE problems with uncertainties, Mingyuan Yang, John T.Foster, Computer Methods in Applied Mechanics and Engineering, 2022. [paper]
- Physics-informed neural networks for high-speed flows, Zhiping Mao, Ameya D. Jagtap, George Em Karniadakis, Computer Methods in Applied Mechanics and Engineering, 2020. [paper]
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data, Luning Sun, Han Gao, Shaowu Pan, Jian-Xun Wang, Computer Methods in Applied Mechanics and Engineering, 2020. [paper]
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations, Maziar Raissi, Alireza Yazdani, George Em Karniadakis, Science, 2020. [paper]
- NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations, Xiaowei Jin, Shengze Cai, Hui Li, George Em Karniadakis, Journal of Computational Physics, 2021. [paper]
- A High-Efficient Hybrid Physics-Informed Neural Networks Based on Convolutional Neural Network, Zhiwei Fang, IEEE Transactions on Neural Networks and Learning Systems, 2021. [paper]
- A Study on a Feedforward Neural Network to Solve Partial Differential Equations in Hyperbolic-Transport Problems, Eduardo Abreu, Joao B. Florindo, ICCS, 2021. [paper]
- A Physics Informed Neural Network Approach to Solution and Identification of Biharmonic Equations of Elasticity, Mohammad Vahab, Ehsan Haghighat, Maryam Khaleghi, Nasser Khalili, arXiv:2108.07243 [cs], 2021. [paper]
- Prediction of porous media fluid flow using physics informed neural networks, Muhammad M. Almajid, Moataz O. Abu-Alsaud, Journal of Petroleum Science and Engineering, 2021. [paper]
- Investigating a New Approach to Quasinormal Modes: Physics-Informed Neural Networks, Anele M. Ncube, Gerhard E. Harmsen, Alan S. Cornell, arXiv:2108.05867 [gr-qc], 2021. [paper]
- Towards neural Earth system modelling by integrating artificial intelligence in Earth system science, Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner, Nature Machine Intelligence, 2021. [paper]
- Physics-informed Neural Network for Nonlinear Dynamics in Fiber Optics, Xiaotian Jiang, Danshi Wang, Qirui Fan, Min Zhang, Chao Lu, Alan Pak Tao Lau, arXiv:2109.00526 [physics], 2021. [paper]
- On Theory-training Neural Networks to Infer the Solution of Highly Coupled Differential Equations, M. Torabi Rad, A. Viardin, M. Apel, arXiv:2102.04890 [physics], 2021. [paper]
- Theory-training deep neural networks for an alloy solidification benchmark problem, M. Torabi Rad, A. Viardin, G. J. Schmitz, M. Apel, arXiv:1912.09800 [physics], 2019. [paper]
- Explicit physics-informed neural networks for nonlinear closure: The case of transport in tissues, Ehsan Taghizadeh, Helen M. Byrne, Brian D. Wood, Journal of Computational Physics, 2022. [paper]
- A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method, Shahed Rezaei, Ali Harandi, Ahmad Moeineddin, Bai-Xiang Xu, Stefanie Reese, arXiv:2206.13103 [cs.CE], 2022. [paper]
- A generalized framework for unsupervised learning and data recovery in computational fluid dynamics using discretized loss functions, Jot Singh Aulakh, Steven B. Beale, and Jon G. Pharoah, Physics of Fluids, 2022. [paper]
- Physics-Informed Neural Networks for AC Optimal Power Flow, Rahul Nellikkath, Spyros Chatzivasileiadis, Electric Power Systems Research, 2022. [paper]
- Physics-informed neural networks for the shallow-water equations on the sphere, Alex Bihlo, Roman O.Popovych, Journal of Computational Physics, 2022. [paper]
- A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature, Gyouho Cho, Mengqi Wang, Youngki Kim, Jaerock Kwon, Wencong Su, IEEE Access, 2022. [paper]
- Physically guided deep learning solver for time-dependent Fokker–Planck equation, Yang Zhang, Ka-Veng Yuen, International Journal of Non-Linear Mechanics, 2022. [paper]
- A Physically Consistent Framework for Fatigue Life Prediction using Probabilistic Physics-Informed Neural Network, Taotao Zhou, Shan Jiang, Te Han, Shun-Peng Zhu, Yinan Cai, International Journal of Fatigue, 2022. [paper]
- Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks, Danial Amini, Ehsan Haghighat, Ruben Juanes, Arxiv, 2022. [paper][code)]
- Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs, Siddhartha Mishra, Roberto Molinaro, IMA Journal of Numerical Analysis, 2021. [paper]
- Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs, Tim De Ryck, Siddhartha Mishra, arXiv:2106.14473 [cs, math], 2021. [paper]
- Error Analysis of Deep Ritz Methods for Elliptic Equations, Yuling Jiao, Yanming Lai, Yisu Luo, Yang Wang, Yunfei Yang, arXiv:2107.14478 [cs, math], 2021. [paper]
- Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces, George Stepaniants, arXiv:2108.11580 [cs, math, stat], 2021. [paper]
- Simultaneous Neural Network Approximations in Sobolev Spaces, Sean Hon, Haizhao Yang, arXiv:2109.00161 [cs, math], 2021. [paper]
- Characterizing possible failure modes in physics-informed neural networks, Aditi S. Krishnapriyan, Amir Gholami, Sh Zhe, ian, Robert M. Kirby, Michael W. Mahoney, arXiv:2109.01050 [physics], 2021. [paper]
- Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks, Sifan Wang, Yujun Teng, Paris Perdikaris, SIAM Journal on Scientific Computing, 2021. [paper]
- Variational Physics Informed Neural Networks: the role of quadratures and test functions, Stefano Berrone, Claudio Canuto, Moreno Pintore, arXiv:2109.02035 [cs, math], 2021. [paper]
- Convergence Analysis for the PINNs, Yuling Jiao, Yanming Lai, Dingwei Li, Xiliang Lu, Yang Wang, Jerry Zhijian Yang, arXiv:2109.01780 [cs, math], 2021. [paper]
- Characterizing possible failure modes in physics-informed neural networks, Aditi Krishnapriyan, Amir Gholami, Sh Zhe, ian, Robert Kirby, Michael W. Mahoney, NIPS, 2021. [paper]
- Convergence rate of DeepONets for learning operators arising from advection-diffusion equations, Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis, arXiv:2102.10621 [math], 2021. [paper]
- Estimates on the generalization error of physics-informed neural networks for approximating PDEs, Siddhartha Mishra, Roberto Molinaro, IMA Journal of Numerical Analysis, 2022. [paper]
- Investigating and Mitigating Failure Modes in Physics-informed Neural Networks (PINNs), Shamsulhaq Basir, arXiv:2209.09988v1[cs], 2022. [paper][code]