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20212026
most citedLearning constitutive models from microstructural simulations via a non-intrusive reduced basis method

21 citations · 29 across the 5 of their papers we have counts for

collaborators

5 papers

math.NA2026

Residual-Driven Lifting Identification for Nonlinear-Manifold Reduced-Order Models of Parametrized Linear PDEs

Francesco A. B. Silva, Jean C. Ragusa, Theron Guo +1

We introduce a residual-driven procedure for training nonlinear-manifold reduced-order models for parametrized linear partial differential equations that, given prescribed latent a…

math.NA2024

An adaptive hierarchical ensemble Kalman filter with reduced basis models

Francesco A. B. Silva, Cecilia Pagliantini, Karen Veroy

The use of model order reduction techniques in combination with ensemble-based methods for estimating the state of systems described by nonlinear partial differential equations has…

math.NA2022

A Reduced Basis Ensemble Kalman Method

Francesco A. B. Silva, Cecilia Pagliantini, Martin Grepl +1

In the process of reproducing the state dynamics of parameter dependent distributed systems, data from physical measurements can be incorporated into the mathematical model to redu…

cs.CE2022★ 8 cited

Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method: Extension to geometrical parameterizations

Theron Guo, Francesco A. B. Silva, Ondřej Rokoš +1

Understanding structure-property relations is essential to optimally design materials for specific applications. Two-scale simulations are often employed to analyze the effect of t…

cs.CE2021★ 21 cited

Learning constitutive models from microstructural simulations via a non-intrusive reduced basis method

Theron Guo, Ondřej Rokoš, Karen Veroy

In order to optimally design materials, it is crucial to understand the structure-property relations in the material by analyzing the effect of microstructure parameters on the mac…