172 citations · 525 across the 23 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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 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…
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…
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…
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…
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…