Physics-informed neural networks for solving parametric magnetostatic problems

8 February 2022

Papers citing "Physics-informed neural networks for solving parametric magnetostatic problems"

9 / 9 papers shown

Title
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods Lise Le Boudec Emmanuel de Bezenac Louis Serrano Ramon Daniel Regueiro-Espino Yuan Yin Patrick Gallinari AI4CE 30 2 0 09 Oct 2024
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks D. Anton Jendrik-Alexander Tröger Henning Wessels Ulrich Römer Alexander Henkes Stefan Hartmann AI4CE 24 4 0 28 May 2024
Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks T. Şahin M. Danwitz A. Popp PINN 14 19 0 24 Aug 2023
Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification A. Alberts Ilias Bilionis 26 12 0 18 Jan 2023
Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data D. Anton Henning Wessels AI4CE 20 7 0 15 Dec 2022
Model-agnostic stochastic model predictive control Tapas Tripura S. Chakraborty 8 5 0 23 Nov 2022
$NVIDIA SimNet^{TM}: an AI-accelerated multi-physics simulation framework$ 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 91 125 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 170 755 0 13 Mar 2020
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 117 506 0 11 Mar 2020