activity
20162026
most citedNeural networks meet hyperelasticity: A guide to enforcing physics

172 citations · 620 across the 24 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

cond-mat.mtrl-sci2023★ 10 cited

Rate- and temperature-dependent ductile-to-brittle fracture transition: Experimental investigation and phase-field analysis for toffee

Franz Dammaß, Dennis Schab, Harald Rohm +1

The mechanical behaviour of many materials, including polymers or natural materials, significantly depends on the rate of deformation. As a consequence, a rate-dependent ductile-to…

cond-mat.mtrl-sci2023★ 4 cited

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…

cs.CE2023★ 3 cited

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…

cond-mat.mtrl-sci2023

Fatigue crack growth in anisotropic aluminium sheets -- phase-field modelling and experimental validation

Martha Kalina, Vanessa Schöne, Boris Spak +3

Fatigue crack growth is decisive for the design of thin-walled structures such as fuselage shells of air planes. The cold rolling process, used to produce the aluminium sheets this…

cs.CE2023★ 74 cited

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…

cs.CE2023★ 172 cited

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…

cond-mat.mtrl-sci2023★ 89 cited

Overview of phase-field models for fatigue fracture in a unified framework

Martha Kalina, Tom Schneider, Jörg Brummund +1

In the last ten years, the phase-field method has gained much attention as a novel method to simulate fracture due to its straightforward way allowing to cover crack initiation and…

cond-mat.mtrl-sci2023★ 36 cited

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…