A Review on Data-Driven Constitutive Laws for Solids

doi:10.1007/s11831-024-10196-2

A Review on Data-Driven Constitutive Laws for Solids

2024-11-03

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AI

J

Jan N. Fuhg

G

Govinda Anantha Padmanabha

DOI：10.1007/s11831-024-10196-2

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Abstract

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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.

Keywords:

CONSISTENT CLUSTERING ANALYSIS

ARTIFICIAL NEURAL-NETWORKS

DEEP MATERIAL NETWORK

STRESS-STRAIN BEHAVIOR

MODEL ORDER REDUCTION

SMALL PUNCH TEST

DUCTILE DAMAGE

FLOW-STRESS

FUZZY DATA

BIG DATA

A Review on Data-Driven Constitutive Laws for Solids

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