Knut Andreas Meyer
My research group discovers computational models that are faster and more accurate than the current state of the art by combining physical laws, engineering experience, and machine learning. These new models make previously impossible simulations possible and provide engineers with new tools to predict and prevent structural failure, optimize designs, and prolong the lifespan of components. In particular, we focus on modeling the nonlinear and history-dependent mechanical behavior of engineering materials subjected to extreme loads, resulting in large plastic deformations and ductile failure. The machine learning-based models require training data, which we obtain by combining physical experiments with physics-based micromechanical simulations. For the latter, we develop advanced finite element simulations using the open-source Ferrite.jl package in the Julia programming language.
Showing 23 publications
Cycle-domain plasticity modeling using neural networks and symbolic regression
Automated model discovery of finite strain elastoplasticity from uniaxial experiments
Influence of a highly deformed surface layer on RCF predictions for rails in service
Effects of predeformation on torsional fatigue in R260 rail steel
The role of accumulated plasticity on yield surface evolution in pearlitic steel
CRACK INITIATION CRITERIA FOR DEFORMED ANISOTROPIC R260 RAIL STEEL
A method for in-field railhead crack detection using digital image correlation
Efficient 3d finite element modeling of cyclic elasto-plastic rolling contact
A distortional hardening model for finite plasticity
Anisotropic yield surfaces after large shear deformations in pearlitic steel
Evaluation of material models describing the evolution of plastic anisotropy in pearlitic steel
Modeling of kinematic hardening at large biaxial deformations in pearlitic rail steel
Characterization of deformed pearlitic rail steel
A comparison of two frameworks for kinematic hardening in hyperelasto-plasticity
MODELING EVOLUTION OF ANISOTROPY IN PEARLITIC STEEL DURING COLD WORKING
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Showing 5 research projects
Constitutive Model Discovery from Physics-Enforced Neural Networks
Sprickinitiering i anisotropa hjul- och rälmaterial
Research into enhanced track and switch and crossing system 2 (In2Track-2)
Influence of anisotropy on deterioration of rail materials (CHARMEC MU34)