Data-Driven Discovery of Composition-Dependent Constitutive Models for Hyperelasticity and Viscoelasticity of Digital Materials

Authors:Josué García-Ávila (1), Beijun Shen (1), Manuel K. Rausch (2 and 3 and 4), Mary C. Boyce (1), Adrián Buganza-Tepole (1) ((1) Department of Mechanical Engineering, Columbia University, New York City, USA, (2) Department of Aerospace Engineering and Engineering Mechanics, University of Texas at Austin, Austin, USA, (3) Department of Biomedical Engineering, University of Texas at Austin, Austin, USA, (4) Department of Mechanical Engineering, University of Texas at Austin, Austin, USA)

Abstract:Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective responses that span more than an order of magnitude in apparent stiffness and exhibit strongly nonlinear, composition-dependent, and rate-dependent dissipative behavior. Classical finite-strain viscoelastic models represent such behavior with closed-form strain energy functions for equilibrium and non-equilibrium stresses as well as evolution of internal variables, which may limit flexibility when a single constitutive model is expected to generalize across materials and loading rates. Here, we present a data-driven multi-material constitutive modeling framework that generalizes a formulation by Bergström and Boyce. The proposed framework retains the structure of the classical model, namely multiplicative kinematics, invariant-based strain-energy functions, and a scalar dissipative evolution law directed along the normalized nonequilibrium deviatoric stress. For the equilibrium branch, the data-driven discovery framework either directly predicts closed-form model parameters as functions of composition or automatically constructs a polyconvex strain-energy function using neural ordinary differential equations (NODEs). The nonequilibrium branch kinetics are learned similarly, either by directly identifying closed-form parameters across compositions or by using appropriately constrained artificial neural networks. Using multi-rate uniaxial compression data across multiple material compositions, we show that the proposed formulation captures rate-dependent stiffness and hysteresis across compositions while preserving thermodynamic consistency.

40 pages including references, 9 figures in the main manuscript. Supplementary material is available upon request from the corresponding author

Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci); Soft Condensed Matter (cond-mat.soft)

arXiv:2609.04541 [cs.AI]

(or arXiv:2609.04541v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2609.04541 arXiv-issued DOI via DataCite (pending registration)