Fast PDE-constrained optimization via self-supervised operator learning

25 October 2021

Papers citing "Fast PDE-constrained optimization via self-supervised operator learning"

19 / 19 papers shown

Title
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 56 0 0 21 Feb 2025
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models Matteo Tomasetto Andrea Manzoni Francesco Braghin AI4CE 13 1 0 09 Sep 2024
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 29 42 0 09 Jul 2024
Neural Born Series Operator for Biomedical Ultrasound Computed Tomography Zhijun Zeng Yihang Zheng Youjia Zheng Yubing Li Zuoqiang Shi He Sun 22 1 0 25 Dec 2023
Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems Yongcun Song Xiaoming Yuan Hangrui Yue AI4CE 8 2 0 01 Jul 2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators Dingcheng Luo Thomas O'Leary-Roseberry Peng Chen Omar Ghattas AI4CE 19 15 0 31 May 2023
A DeepONet multi-fidelity approach for residual learning in reduced order modeling N. Demo M. Tezzele G. Rozza 12 16 0 24 Feb 2023
VI-DGP: A variational inference method with deep generative prior for solving high-dimensional inverse problems Yingzhi Xia Qifeng Liao Jinglai Li 11 2 0 22 Feb 2023
The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach Yongcun Song Xiaoming Yuan Hangrui Yue PINN 17 0 0 16 Feb 2023
AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios Zhongzhan Huang Mingfu Liang Liang Lin Liang Lin 13 5 0 05 Feb 2023
Physics-guided Data Augmentation for Learning the Solution Operator of Linear Differential Equations Yemo Li Yiwen Pang Bin Shan AI4CE 18 3 0 08 Dec 2022
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications Zhongkai Hao Songming Liu Yichi Zhang Chengyang Ying Yao Feng Hang Su Jun Zhu PINN AI4CE 21 87 0 15 Nov 2022
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems Lianghao Cao Thomas O'Leary-Roseberry Prashant K. Jha J. Oden Omar Ghattas 16 26 0 06 Oct 2022
Physics-informed neural networks for PDE-constrained optimization and control Jostein Barry-Straume A. Sarshar Andrey A. Popov Adrian Sandu PINN AI4CE 10 14 0 06 May 2022
Learning Operators with Coupled Attention Georgios Kissas Jacob H. Seidman Leonardo Ferreira Guilhoto V. Preciado George J. Pappas P. Perdikaris 19 107 0 04 Jan 2022
Quantum Model-Discovery Niklas Heim Atiyo Ghosh Oleksandr Kyriienko V. Elfving 17 11 0 11 Nov 2021
Improved architectures and training algorithms for deep operator networks Sifan Wang Hanwen Wang P. Perdikaris AI4CE 42 103 0 04 Oct 2021
Physics-informed neural networks with hard constraints for inverse design Lu Lu R. Pestourie Wenjie Yao Zhicheng Wang F. Verdugo Steven G. Johnson PINN 39 489 0 09 Feb 2021
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks Sifan Wang Hanwen Wang P. Perdikaris 131 437 0 18 Dec 2020