Loss Landscape Engineering via Data Regulation on PINNs

16 May 2022

Papers citing "Loss Landscape Engineering via Data Regulation on PINNs"

7 / 7 papers shown

Title
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs Simone Brivio S. Fresca Andrea Manzoni AI4CE 38 6 0 14 May 2024
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective Sunwoong Yang Hojin Kim Y. Hong K. Yee R. Maulik Namwoo Kang PINN AI4CE 23 17 0 05 Jan 2024
Unsupervised Random Quantum Networks for PDEs Josh Dees Antoine Jacquier Sylvain Laizet 21 2 0 21 Dec 2023
Physics-guided training of GAN to improve accuracy in airfoil design synthesis Kazunari Wada Katsuyuki Suzuki Kazuo Yonekura AI4CE 24 11 0 19 Aug 2023
Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil Rahul Sundar Dipanjan Majumdar Didier Lucor Sunetra Sarkar PINN AI4CE 25 6 0 23 Jun 2023
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results Nicholas Sung Jian Cheng Wong C. Ooi Abhishek Gupta P. Chiu Yew-Soon Ong PINN 20 6 0 15 Dec 2022
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 180 759 0 13 Mar 2020