The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach

16 February 2023

Papers citing "The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach"

6 / 6 papers shown

Title
FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning Yongcun Song Ziqi Wang Enrique Zuazua FedML 35 2 0 21 Feb 2024
Accelerated primal-dual methods with enlarged step sizes and operator learning for nonsmooth optimal control problems Yongcun Song Xiaoming Yuan Hangrui Yue AI4CE 19 2 0 01 Jul 2023
Solving PDE-constrained Control Problems Using Operator Learning Rakhoon Hwang Jae Yong Lee J. Shin H. Hwang AI4CE 117 43 0 09 Nov 2021
Physics and Equality Constrained Artificial Neural Networks: Application to Forward and Inverse Problems with Multi-fidelity Data Fusion S. Basir Inanc Senocak PINN AI4CE 34 68 0 30 Sep 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 50 494 0 09 Feb 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 760 0 13 Mar 2020