Neural integration for constitutive equations using small data

Abstract

Data-driven models based on deep learning algorithms intend to overcome the limitations of traditional constitutive modelling by directly learning from data. However, the need for extensive data that collate the full state of the material is hindered by traditional experimental observations, which typically provide only small data - sparse and partial material state observations. To address this issue, we develop a novel deep learning algorithm referred to as Neural Integration for Constitutive Equations to discover constitutive models at the material point level from scarce and incomplete observations. It builds upon the solution of the initial value problem describing the time evolution of the material state, unlike the majority of data-driven approaches for constitutive modelling that require large data of increments of state variables. Numerical benchmarks demonstrate that the method can learn accurate, consistent, and robust constitutive models from incomplete, sparse, and noisy data collecting simple conventional experimental protocols.

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Cited by in corpus (3)

• Precise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks
• Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints
• A physics-augmented neural network framework for finite strain incompressible viscoelasticity