Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows

1 November 2022

Papers citing "Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows"

7 / 7 papers shown

Title
Adaptive Physics-informed Neural Networks: A Survey Edgar Torres Jonathan Schiefer Mathias Niepert PINN AI4CE 63 0 0 23 Mar 2025
Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space Pedro Tarancón-Álvarez Pablo Tejerina-Pérez Raul Jimenez P. Protopapas AI4CE PINN 39 0 0 21 Jan 2025
Zero-shot Imputation with Foundation Inference Models for Dynamical Systems Patrick Seifner K. Cvejoski Ramses J. Sanchez Ramsés J. Sánchez AI4TS AI4CE 18 3 0 12 Feb 2024
One-Shot Transfer Learning for Nonlinear ODEs Wanzhou Lei P. Protopapas Joy Parikh PINN 21 1 0 25 Nov 2023
Understanding and Mitigating Extrapolation Failures in Physics-Informed Neural Networks Lukas Fesser Luca DÁmico-Wong Richard Qiu 30 4 0 15 Jun 2023
Mixed formulation of physics-informed neural networks for thermo-mechanically coupled systems and heterogeneous domains Ali Harandi Ahmad Moeineddin Michael Kaliske Stefanie Reese Shahed Rezaei AI4CE PINN 22 42 0 09 Feb 2023
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks Blake Bullwinkel Dylan Randle P. Protopapas David Sondak 24 3 0 15 Sep 2022