Enhancing phenomenological yield functions with data: challenges and opportunities

DOI10.1016/j.euromechsol.2023.104925OpenAlexW4316369652MaRDI QIDQ2692822

Jan N. Fuhg, Nikolaos Bouklas, Michele Marino, Amelie Fau

Publication date: 17 March 2023

Published in: European Journal of Mechanics. A. Solids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.euromechsol.2023.104925

zbMATH Keywords

elastoplasticitymachine learningiterative solution schemeconvex yield functionelasto-plastic surrogate modelmodel-data-driven yield functionstress-strain constitutive response

Mathematics Subject Classification ID

Small-strain, rate-independent theories of plasticity (including rigid-plastic and elasto-plastic materials) (74C05) Numerical and other methods in solid mechanics (74S99)

Related Items (1)

Modular machine learning-based elastoplasticity: generalization in the context of limited data

Uses Software

• CVXPY
• PyTorch
• Adam
• F3DAM

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonparametric least squares estimation of a multivariate convex regression function
• The design and analysis of computer experiments.
• Big data in experimental mechanics and model order reduction: today's challenges and tomorrow's opportunities
• Plasticity-damage couplings: from single crystal to polycrystalline materials
• Linear transformation-based anisotropic yield functions.
• A criterion for description of anisotropy and yield differential effects in pressure-insensitive metals
• Sobolev training of thermodynamic-informed neural networks for interpretable elasto-plasticity models with level set hardening
• Anisotropic hyperelastic constitutive models for finite deformations combining material theory and data-driven approaches with application to cubic lattice metamaterials
• Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
• The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
• On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
• A machine learning based plasticity model using proper orthogonal decomposition
• Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
• Model-data-driven constitutive responses: application to a multiscale computational framework
• Unsupervised discovery of interpretable hyperelastic constitutive laws
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A kinetic two-scale damage model for high-cycle fatigue simulation using multi-temporal Latin framework
• Machine learning constitutive models of elastomeric foams
• CVXPY: A Python-Embedded Modeling Language for Convex Optimization
• Consistency of Multidimensional Convex Regression
• RELIABLE INTEGRATION OF CONTINUOUS CONSTRAINTS INTO EXTREME LEARNING MACHINES
• Representation theorem for convex nonparametric least squares
• Interior-point methods for optimization
• Advanced Lectures on Machine Learning
• A theory of the yielding and plastic flow of anisotropic metals
• Large scale kernel regression via linear programming

This page was built for publication: Enhancing phenomenological yield functions with data: challenges and opportunities