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"

7 / 7 papers shown

Title
Differentiable programming across the PDE and Machine Learning barrier N. Bouziani David A. Ham Ado Farsi PINN AI4CE 59 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 93 4 0 28 May 2024
Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks Siyuan Song Hanxun Jin AI4CE PINN 58 8 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 51 1 0 07 Aug 2023
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks Inbar Seroussi Asaf Miron Zohar Ringel PINN 108 0 0 12 Jul 2023
Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data D. Anton Henning Wessels AI4CE 63 8 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 78 50 0 15 Oct 2022