hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition

11 March 2020

Papers citing "hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition"

7 / 7 papers shown

Title
Reliable and Efficient Inverse Analysis using Physics-Informed Neural Networks with Distance Functions and Adaptive Weight Tuning Shota Deguchi Mitsuteru Asai PINN AI4CE 71 0 0 25 Apr 2025
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations Hamidreza Eivazi Jendrik-Alexander Tröger Stefan H. A. Wittek Stefan Hartmann Andreas Rausch AI4CE 41 0 0 27 Mar 2025
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks Youngsik Hwang Dong-Young Lim AI4CE 20 2 0 27 Sep 2024
A Primer on Variational Inference for Physics-Informed Deep Generative Modelling Alex Glyn-Davies A. Vadeboncoeur O. Deniz Akyildiz Ieva Kazlauskaite Mark Girolami PINN 47 0 0 10 Sep 2024
Exact imposition of boundary conditions with distance functions in physics-informed deep neural networks N. Sukumar Ankit Srivastava PINN AI4CE 32 239 0 17 Apr 2021
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 616 0 13 Mar 2020
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 118 1,010 0 27 Aug 2019