238 citations · 530 across the 13 of their papers we have counts for
9 papers · 1 filter
Digital twin inference from multi-physical simulation data of DED additive manufacturing processes with neural ODEs
Maximilian Kannapinn, Fabian Roth, Oliver Weeger
A digital twin is a virtual representation that accurately replicates its physical counterpart, fostering bi-directional real-time data exchange throughout the entire process lifec…
Multiscale topology optimization of functionally graded lattice structures based on physics-augmented neural network material models
Jonathan Stollberg, Tarun Gangwar, Oliver Weeger +1
We present a new framework for the simultaneous optimiziation of both the topology as well as the relative density grading of cellular structures and materials, also known as latti…
TwinLab: a framework for data-efficient training of non-intrusive reduced-order models for digital twins
Maximilian Kannapinn, Michael Schäfer, Oliver Weeger
Purpose: Simulation-based digital twins represent an effort to provide high-accuracy real-time insights into operational physical processes. However, the computation time of many m…
Physics-augmented neural networks for constitutive modeling of hyperelastic geometrically exact beams
Jasper O. Schommartz, Dominik K. Klein, Juan C. Alzate Cobo +1
We present neural network-based constitutive models for hyperelastic geometrically exact beams. The proposed models are physics-augmented, i.e., formulated to fulfill important mec…
Nonlinear electro-elastic finite element analysis with neural network constitutive models
Dominik K. Klein, Rogelio Ortigosa, Jesús Martínez-Frutos +1
In the present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the i…
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…
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…