Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data

15 December 2022

Papers citing "Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data"

5 / 5 papers shown

Title
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 26 4 0 28 May 2024
Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations Benjamin Moseley Andrew Markham T. Nissen‐Meyer PINN 37 207 0 16 Jul 2021
Efficient training of physics-informed neural networks via importance sampling M. A. Nabian R. J. Gladstone Hadi Meidani DiffM PINN 69 220 0 26 Apr 2021
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
An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications E. Samaniego C. Anitescu S. Goswami Vien Minh Nguyen-Thanh Hongwei Guo Khader M. Hamdia Timon Rabczuk X. Zhuang PINN AI4CE 145 1,333 0 27 Aug 2019