activity
20192023
most citedMachine learning-based sampling of virtual experiments within the full stress state

26 citations · 65 across the 6 of their papers we have counts for

collaborators

6 papers

cond-mat.mtrl-sci2023★ 3 cited

Neural Networks for Constitutive Modeling -- From Universal Function Approximators to Advanced Models and the Integration of Physics

Johannes Dornheim, Lukas Morand, Hemanth Janarthanam Nallani +1

Analyzing and modeling the constitutive behavior of materials is a core area in materials sciences and a prerequisite for conducting numerical simulations in which the material beh…

cond-mat.mtrl-sci2022★ 26 cited

Machine learning-based sampling of virtual experiments within the full stress state

Alexander Wessel, Lukas Morand, Alexander Butz +2

This paper presents a new machine learning-based approach to investigate anisotropic yield surfaces of sheet metals by means of virtual experiments. The new sampling approach is ba…

cond-mat.mtrl-sci2021

A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization

Tarek Iraki, Lukas Morand, Johannes Dornheim +2

The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science. In this sense, processes are supposed t…

cs.LG2020★ 26 cited

Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization

Johannes Dornheim, Lukas Morand, Samuel Zeitvogel +3

A major goal of materials design is to find material structures with desired properties and in a second step to find a processing path to reach one of these structures. In this pap…

cs.LG2020★ 1 cited

CupNet -- Pruning a network for geometric data

Raoul Heese, Lukas Morand, Dirk Helm +1

Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network i…

cs.LG2019★ 9 cited

The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge

Raoul Heese, Michał Walczak, Lukas Morand +2

We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box…