PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs

PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs

23 September 2019

George Karniadakis

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Papers citing "PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs"

18 / 18 papers shown

Title
Uncertainty propagation in feed-forward neural network models Jeremy Diamzon Daniele Venturi 106 0 0 27 Mar 2025
Physics-informed deep learning for infectious disease forecasting Y. Qian Éric Marty Avranil Basu Avranil Basu Eamon B. O'Dea Xianqiao Wang Spencer Fox Pejman Rohani John M. Drake He Li PINN AI4CE 129 2 0 16 Jan 2025
DeepXDE: A deep learning library for solving differential equations Lu Lu Xuhui Meng Zhiping Mao George Karniadakis PINN AI4CE 97 1,533 0 10 Jul 2019
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning P. Stinis AI4TS AI4CE 55 11 0 17 May 2019
Solving Irregular and Data-enriched Differential Equations using Deep Neural Networks C. Michoski M. Milosavljević Todd A. Oliver D. Hatch AI4CE 37 22 0 10 May 2019
Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks Dongkun Zhang Ling Guo George Karniadakis AI4CE 73 213 0 03 May 2019
Physical Symmetries Embedded in Neural Networks M. Mattheakis P. Protopapas D. Sondak Marco Di Giovanni E. Kaxiras PINN 51 71 0 18 Apr 2019
Physics-Informed Echo State Networks for Chaotic Systems Forecasting N. Doan W. Polifke Luca Magri 51 40 0 09 Apr 2019
Analyzing Machine Learning Workloads Using a Detailed GPU Simulator Jonathan Lew Deval Shah Suchita Pati Shaylin Cattell Mengchi Zhang ... Christopher Ng Negar Goli Matthew D. Sinclair Timothy G. Rogers Tor M. Aamodt 58 65 0 18 Nov 2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yibo Yang P. Perdikaris AI4CE PINN 129 359 0 09 Nov 2018
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 125 366 0 05 Nov 2018
Exascale Deep Learning for Climate Analytics Thorsten Kurth Sean Treichler Josh Romero M. Mudigonda Nathan Luehr ... Michael A. Matheson J. Deslippe M. Fatica P. Prabhat Michael Houston BDL 65 262 0 03 Oct 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 111 409 0 21 Sep 2018
Doing the impossible: Why neural networks can be trained at all Nathan Oken Hodas P. Stinis AI4CE 38 20 0 13 May 2018
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification Rohit Tripathy Ilias Bilionis AI4CE 65 410 0 02 Feb 2018
Optimization Methods for Large-Scale Machine Learning Léon Bottou Frank E. Curtis J. Nocedal 246 3,216 0 15 Jun 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.9K 150,115 0 22 Dec 2014
On the Computational Efficiency of Training Neural Networks Roi Livni Shai Shalev-Shwartz Ohad Shamir 143 480 0 05 Oct 2014

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