238 citations · 477 across the 6 of their papers we have counts for
6 papers
Parametrised polyconvex hyperelasticity with physics-augmented neural networks
Dominik K. Klein, Fabian J. Roth, Iman Valizadeh +1
In the present work, neural networks are applied to formulate parametrised hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity…
Advanced discretization techniques for hyperelastic physics-augmented neural networks
Marlon Franke, Dominik K. Klein, Oliver Weeger +1
In the present work, advanced spatial and temporal discretization techniques are tailored to hyperelastic physics-augmented neural networks, i.e., neural network based constitutive…
Neural networks meet hyperelasticity: A guide to enforcing physics
Lennart Linden, Dominik K. Klein, Karl A. Kalina +3
In the present work, a hyperelastic constitutive model based on neural networks is proposed which fulfills all common constitutive conditions by construction, and in particular, is…
Neural Networks for Chess
Dominik Klein
AlphaZero, Leela Chess Zero and Stockfish NNUE revolutionized Computer Chess. This book gives a complete introduction into the technical inner workings of such engines. The book is…
Finite electro-elasticity with physics-augmented neural networks
Dominik K. Klein, Rogelio Ortigosa, Jesús Martínez-Frutos +1
In the present work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. Using different sets of inva…
Polyconvex anisotropic hyperelasticity with neural networks
Dominik K. Klein, Mauricio Fernández, Robert J. Martin +2
In the present work, two machine learning based constitutive models for finite deformations are proposed. Using input convex neural networks, the models are hyperelastic, anisotrop…