Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID

Abstract

We propose an automated computational algorithm for simultaneous model selection and parameter identification for the hyperelastic mechanical characterization of human brain tissue. Following the motive of the recently proposed computational framework EUCLID (Efficient Unsupervised Constitutive Law Identitication and Discovery) and in contrast to conventional parameter calibration methods, we construct an extensive set of candidate hyperelastic models, i.e., a model library including popular models known from the literature, and develop a computational strategy for automatically selecting a model from the library that conforms to the available experimental data while being represented as an interpretable symbolic mathematical expression. This computational strategy comprises sparse regression, i.e., a regression problem that is regularized by a sparsity promoting penalty term that filters out irrelevant models from the model library, and a clustering method for grouping together highly correlated and thus redundant features in the model library. The model selection procedure is driven by labelled data pairs stemming from mechanical tests under different deformation modes, i.e., uniaxial compression/tension and simple torsion, and can thus be interpreted as a supervised counterpart to the originally proposed EUCLID that is informed by full-field displacement data and global reaction forces. The proposed method is verified on synthetical data with artificial noise and validated on experimental data acquired through mechanical tests of human brain specimens, proving that the method is capable of discovering hyperelastic models that exhibit both high fitting accuracy to the data as well as concise and thus interpretable mathematical representations.

arXiv comment28 pages

References in corpus (20)

• Least Angle Regression
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Discussion of "Least angle regression" by Efron et al
• Rejoinder to "Least angle regression" by Efron et al
• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• Data-driven computational mechanics
• A new family of Constitutive Artificial Neural Networks towards automated model discovery
• Unsupervised discovery of interpretable hyperelastic constitutive laws
• NN-EUCLID: deep-learning hyperelasticity without stress data
• Automated discovery of generalized standard material models with EUCLID
• Discovering plasticity models without stress data
• Data-driven Tissue Mechanics with Polyconvex Neural Ordinary Differential Equations
• Finite electro-elasticity with physics-augmented neural networks
• Automated identification of linear viscoelastic constitutive laws with EUCLID

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• Physics-Informed Neural Network-Based Discovery of Hyperelastic Constitutive Models from Extremely Scarce Data
• Biaxial characterization of soft elastomers: experiments and data-adaptive configurational forces for fracture
• A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
• Experimental Learning of a Hyperelastic Behavior with a Physics-Augmented Neural Network
• Unsupervised Constitutive Model Discovery from Sparse and Noisy Data
• Unsupervised Material Fingerprinting: Ultra-fast hyperelastic model discovery from full-field experimental measurements
• Data-adaptive spline surfaces for non-separable hyperelastic energy functions