A discrete physics-informed training for projection-based reduced order models with neural networks

A discrete physics-informed training for projection-based reduced order models with neural networks

31 March 2025

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Papers citing "A discrete physics-informed training for projection-based reduced order models with neural networks"

18 / 18 papers shown

Title
A finite element-based physics-informed operator learning framework for spatiotemporal partial differential equations on arbitrary domains Yusuke Yamazaki Ali Harandi Mayu Muramatsu A. Viardin Markus Apel T. Brepols Stefanie Reese Shahed Rezaei AI4CE 82 13 0 21 May 2024
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs Simone Brivio S. Fresca Andrea Manzoni AI4CE 69 7 0 14 May 2024
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F. Pichi B. Moya J. Hesthaven AI4CE 66 57 0 15 May 2023
Finite Element Method-enhanced Neural Network for Forward and Inverse Problems R. Meethal B. Obst Mohamed Khalil A. Ghantasala A. Kodakkal K. Bletzinger R. Wüchner AI4CE 50 33 0 17 May 2022
Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds Mattia Cenedese Joar Axås Bastian Bäuerlein Kerstin Avila George Haller 55 127 0 13 Jan 2022
Physics-Informed Neural Operator for Learning Partial Differential Equations Zong-Yi Li Hongkai Zheng Nikola B. Kovachki David Jin Haoxuan Chen Burigede Liu Kamyar Azizzadenesheli Anima Anandkumar AI4CE 121 424 0 06 Nov 2021
$Physics-informed neural networks for solving Reynolds-averaged Navier$\unicode{x2013}$Stokes equations$ Physics-informed neural networks for solving Reynolds-averaged Navier $\unicode{x2013}$ Stokes equations Hamidreza Eivazi M. Tahani P. Schlatter Ricardo Vinuesa PINN AI4CE 62 267 0 22 Jul 2021
Physics-informed neural networks (PINNs) for fluid mechanics: A review Shengze Cai Zhiping Mao Zhicheng Wang Minglang Yin George Karniadakis PINN AI4CE 79 1,198 0 20 May 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 97 707 0 19 Mar 2021
A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder Youngkyu Kim Youngsoo Choi David Widemann T. Zohdi AI4CE 59 198 0 25 Sep 2020
Data-Driven Aerospace Engineering: Reframing the Industry with Machine Learning Steven L. Brunton J. Nathan Kutz Krithika Manohar Aleksandr Aravkin K. Morgansen ... J. Buttrick Jeffrey Poskin Agnes Blom-Schieber Thomas Hogan Darren McDonald AI4CE 46 131 0 24 Aug 2020
Mesh sampling and weighting for the hyperreduction of nonlinear Petrov-Galerkin reduced-order models with local reduced-order bases Sebastian Grimberg C. Farhat R. Tezaur Charbel Bou-Mosleh 64 69 0 06 Aug 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 916 0 28 Jul 2020
Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics Jiayang Xu Karthik Duraisamy AI4CE 56 143 0 23 Dec 2019
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 79 643 0 29 Mar 2019
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data Yinhao Zhu N. Zabaras P. Koutsourelakis P. Perdikaris PINN AI4CE 114 869 0 18 Jan 2019
Collapse of Deep and Narrow Neural Nets Lu Lu Yanhui Su George Karniadakis ODL 71 156 0 15 Aug 2018
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations M. Raissi P. Perdikaris George Karniadakis PINN AI4CE 85 931 0 28 Nov 2017

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