36 citations · 46 across the 5 of their papers we have counts for
5 papers
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…
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…