Calibrating constitutive models with full-field data via physics
informed neural networks

Calibrating constitutive models with full-field data via physics informed neural networks

30 March 2022

Papers citing "Calibrating constitutive models with full-field data via physics informed neural networks"

9 / 9 papers shown

Title
Differentiable programming across the PDE and Machine Learning barrier N. Bouziani David A. Ham Ado Farsi PINN AI4CE 32 1 0 09 Sep 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
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks Siyuan Song Hanxun Jin AI4CE PINN 19 6 0 29 Aug 2023
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables Javier Orera-Echeverria J. Ayensa-Jiménez Manuel Doblaré 18 1 0 07 Aug 2023
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks Inbar Seroussi Asaf Miron Z. Ringel PINN 29 0 0 12 Jul 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
Modular machine learning-based elastoplasticity: generalization in the context of limited data J. Fuhg Craig M. Hamel K. Johnson Reese E. Jones N. Bouklas 14 48 0 15 Oct 2022
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