Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration

27 May 2022

Papers citing "Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration"

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 81 0 0 25 Apr 2025
Practical Aspects on Solving Differential Equations Using Deep Learning: A Primer Georgios Is. Detorakis 28 0 0 21 Aug 2024
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations N. McGreivy Ammar Hakim AI4CE 39 43 0 09 Jul 2024
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 44 17 0 25 Mar 2022
Modeling the Shape of the Brain Connectome via Deep Neural Networks Haocheng Dai M. Bauer P. T. Fletcher S. Joshi MedIm DiffM 17 1 0 06 Mar 2022
Physics-based Deep Learning Nils Thuerey Philipp Holl P. Holl Patrick Schnell Felix Trost Kiwon Um P. Schnell F. Trost PINN AI4CE 56 92 0 11 Sep 2021
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework C. Jiang S. Esmaeilzadeh Kamyar Azizzadenesheli K. Kashinath Mustafa A. Mustafa H. Tchelepi P. Marcus P. Prabhat Anima Anandkumar AI4CE 187 141 0 01 May 2020