83 citations · 191 across the 21 of their papers we have counts for
22 papers
Hyperreduction accelerates finite element model updating for the discovery of neural materials
Hagen Holthusen, Matthias Knipper, Kevin Linka
Finite element model updating is among the most general approaches to discover constitutive models from structural experiments: since the displacement field is computed rather than…
Component-wise hyperreduction for nonlinear solid mechanics problems
Stephan Ritzert, Jannick Kehls, Erik Faust +3
Hyperreduced nonlinear solid-mechanics components can be generated offline and reused as transferable building blocks across different assemblies, boundary conditions, meshes, mate…
Benchmarking data-driven material models on the classic Treloar dataset
Hagen Holthusen, Moritz Flaschel, Denisa Martonová +1
Machine learning is rapidly reshaping constitutive modeling, offers new ways to learn material behavior directly from experimental data, and challenges long-established modeling pa…
Reduced integration with scaled boundary parametrization for virtual elements at finite strains
Njomza Pacolli, Bjorn Sauren, Jannick Kehls +3
This contribution presents an alternative stabilization technique for the virtual element method (VEM) based on reduced integration combined with a scaled boundary parametrization.…
Watching Physics: the Generative Science of Matter and Motion
Hagen Holthusen, Kevin Linka, Ellen Kuhl
Can we learn the physics of matter in motion directly from images and video--and trust it? Answering this question requires integrating experiments, physics-based simulation, and d…
Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
Moritz Flaschel, Hagen Holthusen, Denisa Martonová +1
We recently proposed a method called Material Fingerprinting for the rapid discovery of mechanical material models that avoids solving continuous optimization problems. Material Fi…
Influence of the geometry on the mechanical performance of tubular interlockings: A study of the Sine Block
Domen Macek, Meike Weiß, Reymond Akpanya +3
Topological interlocking assemblies (TIA) are arrangements of blocks such that rigid-body motions of the blocks are fully constrained by their neighbours and a fixed frame. In this…
A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipation
Hagen Holthusen, Paul Steinmann, Ellen Kuhl
We present a physics-based neural network framework for the discovery of constitutive models in fully coupled thermomechanics. In contrast to classical formulations based on the He…
Inelastic Constitutive Kolmogorov-Arnold Networks: A generalized framework for automated discovery of interpretable inelastic material models
Chenyi Ji, Kian P. Abdolazizi, Hagen Holthusen +2
A key problem of solid mechanics is the identification of the constitutive law of a material, that is, the relation between strain history and stress. Machine learning has lead to…
A Complement to Neural Networks for Anisotropic Inelasticity at Finite Strains
Hagen Holthusen, Ellen Kuhl
We propose a complement to constitutive modeling that augments neural networks with material principles to capture anisotropy and inelasticity at finite strains. The key element is…
Autoencoder-based non-intrusive model order reduction in continuum mechanics
Jannick Kehls, Ellen Kuhl, Tim Brepols +2
We propose a non-intrusive, Autoencoder-based framework for reduced-order modeling in continuum mechanics. Our method integrates three stages: (i) an unsupervised Autoencoder compr…
A Comprehensive Framework for Predictive Computational Modeling of Growth and Remodeling in Tissue-Engineered Cardiovascular Implants
Mahmoud Sesa, Hagen Holthusen, Christian Böhm +3
Developing clinically viable tissue-engineered cardiovascular implants remains a formidable challenge. Achieving reliable and durable outcomes requires a deeper understanding of th…
A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains
Hagen Holthusen, Kevin Linka, Ellen Kuhl +1
We present a methodology for designing a generalized dual potential, or pseudo potential, for inelastic Constitutive Artificial Neural Networks (iCANNs). This potential, expressed…
An enhanced single Gaussian point continuum finite element formulation using automatic differentiation
Njomza Pacolli, Ahmad Awad, Jannick Kehls +4
This contribution presents an improved low-order 3D finite element formulation with hourglass stabilization using automatic differentiation (AD). Here, the former Q1STc formulation…
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling
Hagen Holthusen, Tim Brepols, Kevin Linka +1
Soft biological tissues exhibit a tendency to maintain a preferred state of tensile stress, known as tensional homeostasis, which is restored even after external mechanical stimuli…
An anisotropic, brittle damage model for finite strains with a generic damage tensor regularization
Tim van der Velden, Stefanie Reese, Hagen Holthusen +1
This paper establishes a universal framework for the nonlocal modeling of anisotropic damage at finite strains. By the combination of two recent works, the new framework allows for…
Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks
Birte Boes, Jaan-Willem Simon, Hagen Holthusen
In this work, we extend the existing framework of inelastic constitutive artificial neural networks (iCANNs) by incorporating plasticity to increase their applicability to model mo…
Mechanical Comparison of Arrangement Strategies for Topological Interlocking Assemblies
Tom Goertzen, Domen Macek, Lukas Schnelle +4
Topological Interlocking assemblies are arrangements of blocks kinematically constrained by a fixed frame, such that all rigid body motions of each block are constrained only by it…
A comparative study of micromorphic gradient-extensions for anisotropic damage at finite strains
Tim van der Velden, Tim Brepols, Stefanie Reese +1
Modern inelastic material model formulations rely on the use of tensor-valued internal variables. When inelastic phenomena include softening, simulations of the former are prone to…
Theory and implementation of inelastic Constitutive Artificial Neural Networks
Hagen Holthusen, Lukas Lamm, Tim Brepols +2
Nature has always been our inspiration in the research, design and development of materials and has driven us to gain a deep understanding of the mechanisms that characterize aniso…
Mechanical modeling of the maturation process for tissue-engineered implants: application to biohybrid heart valves
Mahmoud Sesa, Hagen Holthusen, Lukas Lamm +4
The development of tissue-engineered cardiovascular implants can improve the lives of large segments of our society who suffer from cardiovascular diseases. Regenerative tissues ar…
A macroscopic approach for stress driven anisotropic growth in bioengineered soft tissues
L. Lamm, H. Holthusen, T. Brepols +2
The simulation of growth processes within soft biological tissues is of utmost importance for many applications in the medical sector. Within this contribution we propose a new mac…