A Review on Data-Driven Constitutive Laws for Solids
Fuhg JN, Anantha Padmanabha G, Bouklas N, Bahmani B, Sun WC, Vlassis NN, Flaschel M, Carrara P, De Lorenzis L (2025)
Publication Type: Journal article, Review article
Publication year: 2025
Journal
Book Volume: 32
Pages Range: 1841-1883
Article Number: 112658
Journal Issue: 3
DOI: 10.1007/s11831-024-10196-2
Abstract
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-dependent response of solids. Our objective is to provide an organized taxonomy to a large spectrum of methodologies developed in the past decades and to discuss the benefits and drawbacks of the various techniques for interpreting and forecasting mechanics behavior across different scales. Distinguishing between machine-learning-based and model-free methods, we further categorize approaches based on their interpretability and on their learning process/type of required data, while discussing the key problems of generalization and trustworthiness. We attempt to provide a road map of how these can be reconciled in a data-availability-aware context. We also touch upon relevant aspects such as data sampling techniques, design of experiment, verification, and validation.
Involved external institutions
University of Texas at Austin
United States (USA) (US)
Cornell University
United States (USA) (US)
Columbia University
United States (USA) (US)
University of California, San Diego (UC San Diego, UCSD)
United States (USA) (US)
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
United States (USA) (US)
Cornell University
United States (USA) (US)
Columbia University
United States (USA) (US)
University of California, San Diego (UC San Diego, UCSD)
United States (USA) (US)
Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich
Switzerland (CH)
How to cite
APA:
Fuhg, J.N., Anantha Padmanabha, G., Bouklas, N., Bahmani, B., Sun, W.C., Vlassis, N.N.,... De Lorenzis, L. (2025). A Review on Data-Driven Constitutive Laws for Solids. Archives of Computational Methods in Engineering, 32(3), 1841-1883. https://doi.org/10.1007/s11831-024-10196-2
MLA:
Fuhg, Jan N., et al. "A Review on Data-Driven Constitutive Laws for Solids." Archives of Computational Methods in Engineering 32.3 (2025): 1841-1883.
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