A Physics-informed Assembly of Feed-Forward Neural Network Engines to Predict Inelasticity in Cross-Linked Polymers

Abstract

In solid mechanics, Data-driven approaches are widely considered as the new paradigm that can overcome the classic problems of constitutive models such as limiting hypothesis, complexity, and high dependence on training data. However, implementation of machine-learned approaches in material modeling has been modest due to the high-dimensionality of the data space, significant size of missing data, and limited convergence. This work proposes a framework to hire concepts from polymer science, statistical physics, and continuum mechanics to provide super-constrained machine-learning techniques of reduced-order to overcome many of the existing difficulties. Using a sequential order-reduction, we have simplified the 3D stress-strain tensor mapping problem into a limited number of super-constrained 1D mapping problems. Next, we introduce an assembly of multiple replicated Neural Network agents to systematically classify those mapping problems into a few categories, all of which are replications of a few distinct agent types. By capturing all loading modes through a simplified set of disperse experimental data, the proposed hybrid assembly of agents provides a new generation of machine learned approaches that simply outperforms most constitutive laws in training data volume, training speed, and accuracy even in complicated loading scenarios. Also, it avoids low interpretability of conventional AI-based models.

References in corpus (8)

• Data-driven computational mechanics
• Model-Free Data-Driven Methods in Mechanics: Material Data Identification and Solvers
• A deep learning framework for solution and discovery in solid mechanics
• A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation
• Mixed-Integer Programming Formulation of a Data-Driven Solver in Computational Elasticity
• Data-driven, structure-based hyperelastic manifolds: A macro-micro-macro approach
• Inverse Modeling of Viscoelasticity Materials using Physics Constrained Learning
• A Bayesian Surrogate Constitutive Model to Estimate Failure Probability of Rubber-Like Materials

Cited by in corpus (6)

• A new family of Constitutive Artificial Neural Networks towards automated model discovery
• Polyconvex anisotropic hyperelasticity with neural networks
• Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks
• A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching
• Recovering Mullins damage hyperelastic behaviour with physics augmented neural networks
• A Bayesian Surrogate Constitutive Model to Estimate Failure Probability of Rubber-Like Materials