Temporal Consistency Loss for Physics-Informed Neural Networks

30 January 2023

Papers citing "Temporal Consistency Loss for Physics-Informed Neural Networks"

6 / 6 papers shown

Title
$Physics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology$ Physics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology Sukirt Thakur H. Mitra A. Ardekani 18 0 0 06 Jun 2024
About optimal loss function for training physics-informed neural networks under respecting causality V. A. Es'kin Danil V. Davydov Ekaterina D. Egorova Alexey O. Malkhanov Mikhail A. Akhukov Mikhail E. Smorkalov PINN 16 7 0 05 Apr 2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions Michael Penwarden Ameya Dilip Jagtap Shandian Zhe George Karniadakis Robert M. Kirby PINN AI4CE 16 57 0 28 Feb 2023
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 40 209 0 16 Jul 2021
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework O. Hennigh S. Narasimhan M. A. Nabian Akshay Subramaniam Kaustubh Tangsali M. Rietmann J. Ferrandis Wonmin Byeon Z. Fang S. Choudhry PINN AI4CE 91 126 0 14 Dec 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 172 758 0 13 Mar 2020