jinns: a JAX Library for Physics-Informed Neural Networks

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019

Earlier work this paper cites.

Fourier neural operator for parametric partial differential equations

Original

Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020

Earlier work this paper cites.

Elvet – a neural network-based differential equation and variational problem solver, 2021

Jack Y. Araz, Juan Carlos Criado, and Michael Spannwosky · 2021

Earlier work this paper cites.

Hyperpinn: Learning parameterized differential equations with physics-informed hypernetworks

Filipe de Avila Belbute-Peres, Yi-fan Chen, and Fei Sha · 2021

Earlier work this paper cites.

Physics-informed machine learning

George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021

Earlier work this paper cites.

DeepXDE: A deep learning library for solving differential equations

Lu Lu, Xuhui Meng, Zhiping Mao, and George Em Karniadakis · 2021

Earlier work this paper cites.

Idrlnet: A physics-informed neural network library

Wei Peng, Jun Zhang, Weien Zhou, Xiaoyu Zhao, Wen Yao, and Xiaoqian Chen · 2021

Cited alongside, same era.

Neuralpde: Automating physics-informed neural networks (pinns) with error approximations, 2021

Original

Kirill Zubov, Zoe McCarthy, Yingbo Ma, Francesco Calisto, Valerio Pagliarino, Simone Azeglio, Luca Bottero, Emmanuel Luján, Valentin Sulzer, Ashutosh Bharambe, Nand Vinchhi, Kaushik Balakrishnan, Devesh Upadhyay, and Chris Rackauckas · 2021

Cited alongside, same era.

Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Salvatore Cuomo, Vincenzo Schiano Di Cola, Fabio Giampaolo, Gianluigi Rozza, Maziar Raissi, and Francesco Piccialli · 2022

Cited alongside, same era.

Pdebench: An extensive benchmark for scientific machine learning

Makoto Takamoto, Timothy Praditia, Raphael Leiteritz, Daniel MacKinlay, Francesco Alesiani, Dirk Pflüger, and Mathias Niepert · 2022

Cited alongside, same era.

Physics-informed neural networks for advanced modeling

Dario Coscia, Anna Ivagnes, Nicola Demo, and Gianluigi Rozza · 2023

Pinnacle: A comprehensive benchmark of physics-informed neural networks for solving pdes

Original

Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, et al · 2023

Later among the works it cites.

Nvidia modulus, 2023

NVIDIA · 2023

Later among the works it cites.

JAX: composable transformations of Python+NumPy programs, 2024

James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2024

Closest in time.

Separable physics-informed neural networks

Junwoo Cho, Seungtae Nam, Hyunmo Yang, Seok-Bae Yun, Youngjoon Hong, and Eunbyung Park · 2024

Closest in time.

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

Juan Francisco Mandujano Reyes, Ting Fung Ma, Ian P McGahan, Daniel J Storm, Daniel P Walsh, and Jun Zhu · 2024

Closest in time.