Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility

15 March 2023

Papers citing "Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility"

6 / 6 papers shown

Title
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks A. Celaya Yimo Wang David T. Fuentes Beatrice Riviere 38 0 0 12 Feb 2025
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics Vineet Jagadeesan Nair PINN 38 0 0 10 Oct 2024
Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks M. Haywood-Alexander Giacamo Arcieri A. Kamariotis Eleni Chatzi 21 1 0 02 Oct 2024
Correctness Verification of Neural Networks Approximating Differential Equations Petros Ellinas Rahul Nellikkath Ignasi Ventura Jochen Stiasny Spyros Chatzivasileiadis 10 1 0 12 Feb 2024
PINNSim: A Simulator for Power System Dynamics based on Physics-Informed Neural Networks Jochen Stiasny Baosen Zhang Spyros Chatzivasileiadis PINN 19 6 0 17 Mar 2023
DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks Christian Moya Guang Lin AI4CE PINN 51 37 0 09 Sep 2021