A Finite Operator Learning Technique for Mapping the Elastic Properties of Microstructures to Their Mechanical Deformations

International Journal for Numerical Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

ABSTRACT To obtain fast solutions for governing physical equations in solid mechanics, we introduce a method that integrates the core ideas of finite element with physics‐informed neural networks and concept operators. We propose directly utilizing available discretized weak form packages to construct loss functions algebraically, thereby demonstrating ability find even presence sharp discontinuities. Our focus is on micromechanics as an example, where knowledge deformation stress fields given heterogeneous microstructure crucial further design applications. The primary parameter under investigation Young's modulus distribution within system. investigations reveal physics‐based training yields higher accuracy compared purely data‐driven approaches unseen microstructures. Additionally, offer two methods improve process obtaining high‐resolution solutions, avoiding need use basic interpolation techniques. first one based autoencoder approach enhance efficiency calculation high resolution grid points. Next, Fourier‐based parametrization utilized address complex 2D 3D problems micromechanics. latter idea aims represent microstructures efficiently using Fourier coefficients. proposed draws from deep energy but generalizes enhances them by learning parametric without relying external data. Compared other operator frameworks, it leverages domain decomposition several ways: (1) uses shape derivatives instead automatic differentiation; (2) automatically includes node connectivity, making solver flexible approximating jumps solution fields; (3) can handle arbitrary shapes enforce boundary conditions. provided some initial comparisons well‐known algorithms, emphasize advantages newly method.

Language: Английский

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Model-free chemomechanical interfaces: History-dependent damage under transient mass diffusion

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 431, P. 117286 - 117286

Published: Aug. 22, 2024

Language: Английский

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Sequential Multi-Scale Modeling Using an Artificial Neural Network-Based Surrogate Material Model for Predicting the Mechanical Behavior of a Li-Ion Pouch Cell Under Abuse Conditions

Batteries, Journal Year: 2024, Volume and Issue: 10(12), P. 425 - 425

Published: Dec. 1, 2024

To analyze the safety behavior of electric vehicles, mechanical simulation models their battery cells are essential. ensure computational efficiency, heterogeneous cell structure is represented by homogenized material models. The required parameters calibrated against several characteristic experiments. As a result, it hardly possible to describe individual components, which reduces level detail. In this work, new data-driven model presented, not only provides but also information about components. For purpose, representative volume element (RVE) created. determine constitutive different characterization tests performed. A novel method for carrying out single-layer compression presented in thickness direction. parameterized RVE subjected large number load cases using first-order homogenization theory. This data basis used train an artificial neural network (ANN), then implemented commercial FEA software LS-DYNA R9.3.1 and thus available as model. stress–strain relationship, outputs condition such thinning separator. validated two three-point-bending test indentation purpose. Finally, influence architecture on effort discussed.

Language: Английский

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An efficient kernel learning-based constitutive model for cyclic plasticity in nonlinear finite element analysis

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 436, P. 117700 - 117700

Published: Dec. 28, 2024

Language: Английский

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Artificial Intelligence Transformation of a Permanent Magnet Synchronous Motor Via a Multiphysics-Informed Deep Operator Network

Published: Jan. 1, 2024

Language: Английский

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Data-driven homogenisation of viscoelastic porous elastomers: feedforward versus knowledge-based neural networks

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: 286, P. 109824 - 109824

Published: Nov. 12, 2024

Language: Английский

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