42 citations · 155 across the 16 of their papers we have counts for
4 papers · 1 filter
Neural Chaos: A Spectral Stochastic Neural Operator
Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields
Building surrogate models with uncertainty quantification capabilities is essential for many engineering applications where randomness, such as variability in material properties,…
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas +6
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-depe…
Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials
Bahador Bahmani, WaiChing Sun
We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse exper…
Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions
Bahador Bahmani, Hyoung Suk Suh, WaiChing Sun
Conventional neural network elastoplasticity models are often perceived as lacking interpretability. This paper introduces a two-step machine learning approach that returns mathema…