[2603.12365] Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws

Condensed Matter > Materials Science arXiv:2603.12365 (cond-mat) [Submitted on 12 Mar 2026 (v1), last revised 26 Apr 2026 (this version, v2)] Title:Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws Authors:Kaushik Bhattacharya, Lianghao Cao, Andrew Stuart View a PDF of the paper titled Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws, by Kaushik Bhattacharya and 2 other authors View PDF HTML (experimental) Abstract: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. To address this issue, we propose a Bayesian optimal experimental design framework to quantify, interpret, and maximize the utility of experimental designs for reliable learning of history-dependent constitutive models. In this framework, the design utility is defined as the expected reduction in parametric uncertainty or the expected information gain. This enables in silico design optimization using simulated data and reduces the cost of physical experiments for reliable parameter identification. We introduce two approximations that...