Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws

ArXi:2603.12365v1 Announce Type: cross History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typically learned from experimental data. With a limited experimental budget, eliciting the full range of responses needed to characterize the constitutive relation can be difficult. As a result, the data can be well explained by a range of parameter choices, leading to parameter estimates that are uncertain or unreliable.