5 papers
Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
Hamidreza Eivazi, Henning Wessels
Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simulations. Its practical use, how…
Data-efficient Bayesian-guided design selection from large candidate sets: Application to hyperelastic stochastic metamaterials
Hooman Danesh, Henning Wessels
From a pool of admissible designs, we aim to identify a structure that achieves a target macroscopic stress response. For each candidate, the response is obtained from a high-fidel…
Mechanical State Estimation with a Polynomial-Chaos-Based Statistical Finite Element Method
Vahab Narouie, Henning Wessels, Fehmi Cirak +1
The Statistical Finite Element Method (statFEM) offers a Bayesian framework for integrating computational models with observational data, thus providing improved predictions for st…
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
David Anton, Jendrik-Alexander Tröger, Henning Wessels +3
The calibration of constitutive models from full-field data has recently gained increasing interest due to improvements in full-field measurement capabilities. In addition to the e…
Reduced and All-at-Once Approaches for Model Calibration and Discovery in Computational Solid Mechanics
Ulrich Römer, Stefan Hartmann, Jendrik-Alexander Tröger +4
In the framework of solid mechanics, the task of deriving material parameters from experimental data has recently re-emerged with the progress in full-field measurement capabilitie…