PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs

14 May 2024

Papers citing "PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs"

30 / 30 papers shown

Title
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective Sunwoong Yang Hojin Kim Y. Hong K. Yee R. Maulik Namwoo Kang PINN AI4CE 36 20 0 05 Jan 2024
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs F. Pichi B. Moya J. Hesthaven AI4CE 52 53 0 15 May 2023
Reliable extrapolation of deep neural operators informed by physics or sparse observations Min Zhu Handi Zhang Anran Jiao George Karniadakis Lu Lu 78 94 0 13 Dec 2022
$$\textit{FastSVD-ML-ROM}$: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications$ $\textit{FastSVD-ML-ROM}$ : A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications G. Drakoulas T. Gortsas G. Bourantas V. Burganos D. Polyzos AI4CE 25 16 0 24 Jul 2022
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling Arka Daw Jie Bu Sizhuang He P. Perdikaris Anuj Karpatne AI4CE 57 47 0 05 Jul 2022
Loss Landscape Engineering via Data Regulation on PINNs Vignesh Gopakumar Stanislas Pamela D. Samaddar PINN 51 17 0 16 May 2022
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 75 17 0 25 Mar 2022
Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity E. Coutinho M. DallÁqua L. McClenny M. Zhong U. Braga-Neto Eduardo Gildin 10 40 0 15 Mar 2022
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models Federico Fatone S. Fresca Andrea Manzoni AI4TS 40 16 0 25 Jan 2022
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks Thomas O'Leary-Roseberry Xiaosong Du A. Chaudhuri J. Martins Karen E. Willcox Omar Ghattas 50 22 0 14 Dec 2021
Improved architectures and training algorithms for deep operator networks Sizhuang He Hanwen Wang P. Perdikaris AI4CE 69 105 0 04 Oct 2021
Characterizing possible failure modes in physics-informed neural networks Aditi S. Krishnapriyan A. Gholami Shandian Zhe Robert M. Kirby Michael W. Mahoney PINN AI4CE 78 626 0 02 Sep 2021
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations Pranshu Pant Ruchi Doshi Pranav Bahl A. Farimani AI4CE 25 80 0 09 Jul 2021
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models S. Fresca Andrea Manzoni AI4CE 40 36 0 10 Jun 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sizhuang He Hanwen Wang P. Perdikaris AI4CE 65 685 0 19 Mar 2021
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations N. R. Franco Andrea Manzoni P. Zunino 55 45 0 10 Mar 2021
POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition S. Fresca Andrea Manzoni AI4CE 40 213 0 28 Jan 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 400 2,355 0 18 Oct 2020
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 96 890 0 28 Jul 2020
Deep learning of thermodynamics-aware reduced-order models from data Quercus Hernandez Alberto Badías D. González Francisco Chinesta Elías Cueto PINN AI4CE 22 79 0 03 Jul 2020
Model Reduction and Neural Networks for Parametric PDEs K. Bhattacharya Bamdad Hosseini Nikola B. Kovachki Andrew M. Stuart 135 323 0 07 May 2020
Structure-preserving neural networks Quercus Hernandez Alberto Badías D. González Francisco Chinesta Elías Cueto PINN 98 70 0 09 Apr 2020
A Close Look at Deep Learning with Small Data Lorenzo Brigato Luca Iocchi 102 140 0 28 Mar 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 208 768 0 13 Mar 2020
A comprehensive deep learning-based approach to reduced order modeling of nonlinear time-dependent parametrized PDEs S. Fresca Luca Dede' Andrea Manzoni AI4CE 35 259 0 12 Jan 2020
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators Lu Lu Pengzhan Jin George Karniadakis 142 2,082 0 08 Oct 2019
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems F. J. Gonzalez Maciej Balajewicz AI4CE 98 139 0 03 Aug 2018
Neural Ordinary Differential Equations T. Chen Yulia Rubanova J. Bettencourt David Duvenaud AI4CE 218 5,024 0 19 Jun 2018
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 122 2,775 0 20 Feb 2015
An implementation of a randomized algorithm for principal component analysis Arthur Szlam Y. Kluger M. Tygert 36 43 0 11 Dec 2014