2 citations · 2 across the 2 of their papers we have counts for
5 papers
Hierarchical Laminates Cannot Reproduce All Effective Properties of Planar Two-Phase Composites: A Counterexample with a 16-Component Potential
Lianghao Cao
Designing a composite requires understanding which effective properties can be achieved through varying its microstructure, i.e., the spatial arrangement of given constituent mater…
Schur-Neural KF: Learned Schur-Consistent Corrections to the Extended Kalman Filter
Min Kim, Lianghao Cao, Soon-Jo Chung +1
We present Schur-Neural KF (SN-KF), a learning-based correction to the extended Kalman filter (EKF) that preserves the probabilistic conditioning interpretation of the EKF. The met…
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…
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…
Learning Memory and Material Dependent Constitutive Laws
Kaushik Bhattacharya, Lianghao Cao, George Stepaniants +2
We propose and study a neural operator framework for learning memory- and material microstructure-dependent constitutive laws for heterogeneous materials. We work in the two-scale…