172 citations · 525 across the 23 of their papers we have counts for
25 papers
Universal norms of partially de Rham -pairs and perfectness in Lubin--Tate Iwasawa theory
Marvin Schneider, Rustam Steingart
We study the behaviour of partially de Rham Selmer groups of -pairs along abelian -adic Lie extensions. In the cyclotomic case we compare them with the universal norms studie…
Reliable training of neural hyperelastic models via full-field data
Konrad Friedrichs, Franz Dammaß, Karl A. Kalina +1
We present a systematic investigation of the robustness and limitations of equilibrium gap-based calibrations for hyperelastic physics-augmented neural networks (PANNs), where we c…
Calibration of neural viscoelastic models via full-field data
Brain M. Riemer, Markus Kästner, Karl A. Kalina
We propose an unsupervised learning framework for calibrating a physics-augmented neural network (PANN) for small-strain viscoelasticity via full-field data. It only requires quant…
On limitations of polyconvexity
Dominik K. Klein, Rogelio Ortigosa, Heinrich T. Roth +4
Polyconvex constitutive modeling is attractive as it guarantees stability of numerical simulations and can improve the generalization behavior of material models. However, in certa…
Advances in polyconvex anisotropic hyperelasticity
Dominik K. Klein, Karl A. Kalina, Rogelio Ortigosa +3
A key challenge in material theory is the formulation of models that satisfy all common mechanical constitutive conditions while retaining sufficient flexibility. In this context,…
Construction of minimal integrity basis for anisotropic hyperelasticity via structural tensors
Brain M. Riemer, Jörg Brummund, Karl A. Kalina +3
We present minimal integrity bases for all common anisotropies in hyperelasticity via the structural tensor concept, which can be used to formulate any algebraic invariant function…
A physics-augmented neural network framework for finite strain incompressible viscoelasticity
Karl A. Kalina, Jörg Brummund, Markus Kästner
We propose a physics-augmented neural network (PANN) framework for finite strain incompressible viscoelasticity within the generalized standard materials theory. The formulation is…
Precise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks
Konrad Friedrichs, Franz Dammaß, Karl A. Kalina +1
We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibl…
A data-driven multiscale scheme for anisotropic finite strain magneto-elasticity
Heinrich T. Roth, Philipp Gebhart, Karl A. Kalina +2
In this work, we develop a neural network-based, data-driven, decoupled multiscale scheme for the modeling of structured magnetically soft magnetorheological elastomers (MREs). On…
A dual-stage constitutive modeling framework based on finite strain data-driven identification and physics-augmented neural networks
Lennart Linden, Karl A. Kalina, Jörg Brummund +2
In this contribution, we present a novel consistent dual-stage approach for the automated generation of hyperelastic constitutive models which only requires experimentally measurab…
When invariants matter: The role of I1 and I2 in neural network models of incompressible hyperelasticity
Franz Dammaß, Karl A. Kalina, Markus Kästner
For the formulation of machine learning-based material models, the usage of invariants of deformation tensors is attractive, since this can a priori guarantee objectivity and mater…
Inverse design of anisotropic microstructures using physics-augmented neural networks
Asghar A. Jadoon, Karl A. Kalina, Manuel K. Rausch +2
Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-…
Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions
Karl A. Kalina, Jörg Brummund, WaiChing Sun +1
We present a data-driven framework for the multiscale modeling of anisotropic finite strain elasticity based on physics-augmented neural networks (PANNs). Our approach allows the e…
Inverse design of spinodoid structures using Bayesian optimization
Alexander Raßloff, Paul Seibert, Karl A. Kalina +1
Tailoring materials to achieve a desired behavior in specific applications is of significant scientific and industrial interest as design of materials is a key driver to innovation…
Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables
Max Rosenkranz, Karl A. Kalina, Jörg Brummund +2
We present an approach for the data-driven modeling of nonlinear viscoelastic materials at small strains which is based on physics-augmented neural networks (NNs) and requires only…
Reconstructing microstructures from statistical descriptors using neural cellular automata
Paul Seibert, Alexander Raßloff, Yichi Zhang +4
The problem of generating microstructures of complex materials in silico has been approached from various directions including simulation, Markov, deep learning and descriptor-base…
Neural network-based multiscale modeling of finite strain magneto-elasticity with relaxed convexity criteria
Karl A. Kalina, Philipp Gebhart, Jörg Brummund +3
We present a framework for the multiscale modeling of finite strain magneto-elasticity based on physics-augmented neural networks (NNs). By using a set of problem specific invarian…
Fast reconstruction of microstructures with ellipsoidal inclusions using analytical descriptors
Paul Seibert, Markus Husert, Maximilian P. Wollner +2
Microstructure reconstruction is an important and emerging aspect of computational materials engineering and multiscale modeling and simulation. Despite extensive research and fast…
A comparative study on different neural network architectures to model inelasticity
Max Rosenkranz, Karl A. Kalina, Jörg Brummund +1
The mathematical formulation of constitutive models to describe the path-dependent, i.e., inelastic, behavior of materials is a challenging task and has been a focus in mechanics r…
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…
Two-stage 2D-to-3D reconstruction of realistic microstructures: Implementation and numerical validation by effective properties
Paul Seibert, Alexander Raßloff, Karl Kalina +4
Realistic microscale domains are an essential step towards making modern multiscale simulations more applicable to computational materials engineering. For this purpose, 3D compute…
Microstructure Characterization and Reconstruction in Python: MCRpy
Paul Seibert, Alexander Raßloff, Karl Kalina +2
Microstructure characterization and reconstruction (MCR) is an important prerequisite for empowering and accelerating integrated computational materials engineering. Much progress…
FE An efficient data-driven multiscale approach based on physics-constrained neural networks and automated data mining
Karl A. Kalina, Lennart Linden, Jörg Brummund +1
Herein, we present a new data-driven multiscale framework called FE which is based on two main keystones: the usage of physics-constrained artificial neural networks…
Phase-field modelling and analysis of rate-dependent fracture phenomena at finite deformation
Franz Dammaß, Karl A. Kalina, Marreddy Ambati +1
Fracture of materials with rate-dependent mechanical behaviour, e.g. polymers, is a highly complex process. For an adequate modelling, the coupling between rate-dependent stiffness…