238 citations · 530 across the 13 of their papers we have counts for
15 papers
Physics-based Machine Learning for Computational Fracture Mechanics
Fadi Aldakheel, Elsayed S. Elsayed, Yousef Heider +1
This study introduces a physics-based machine learning framework for modeling both brittle and ductile fractures. Unlike physics-informed neural networks, which solve partial diffe…
Stable Port-Hamiltonian Neural Networks
Fabian J. Roth, Dominik K. Klein, Maximilian Kannapinn +2
In recent years, nonlinear dynamic system identification using artificial neural networks has garnered attention due to its broad potential applications across science and engineer…
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…
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger +12
This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential e…
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…
Polyconvex neural network models of thermoelasticity
Jan N. Fuhg, Asghar Jadoon, Oliver Weeger +2
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonl…
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…
A robust finite strain isogeometric solid-beam element
Abdullah Shafqat, Oliver Weeger, Bai-Xiang Xu
In this work, an efficient and robust isogeometric three-dimensional solid-beam finite element is developed for large deformations and finite rotations with merely displacements as…
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…
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…