2 citations · 2 across the 6 of their papers we have counts for
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A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables
Mayank Raj, Lianghao Cao, Andrew Stuart +1
The identification of constitutive laws is ubiquitous in engineering: in modeling of materials where experimental data are fitted to mathematical models or learning surrogate model…
Multiscale modeling of materials and neural operators
Kaushik Bhattacharya
Multiscale modeling is essential for understanding the complex behavior of materials. However, accurately transferring all relevant information from one scale to another has remain…
Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
Kaushik Bhattacharya, Lianghao Cao, Andrew Stuart
History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typically learned from experimental data. Wit…
Effective behavior of heterogeneous media governed by strain gradient elasticity
Harkirat Singh, Mayank Raj, Kaushik Bhattacharya
Various mechanical phenomena depend on the length scale, and these have inspired a variety of nonlocal and higher gradient continuum theories. Mechanistically, it is believed that…