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math.NA2026
Cost-Accuracy Trade-offs: Neural Operator vs Classical Numerical Solver
Daniel Zhengyu Huang, Andrew M. Stuart
Neural operators are data-driven models that learn mappings from inputs that parameterize partial differential equations, such as spatially varying coefficients, initial conditions…
cond-mat.mtrl-sci2026★ 2 cited
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…