26 citations · 67 across the 11 of their papers we have counts for
12 papers
Diversity-Aware Batch-Mode Active Learning for Efficient Sampling in Data-Driven Constitutive Modeling
Ronak Shoghi, Lukas Morand, Dirk Helm +1
The constitutive behavior of materials is modeled through relationships between stress, strain, and possibly additional internal variables. This results in relatively high-dimensio…
Particle Trajectory Prediction in Discrete Element Simulations using a Graph-Based Interaction-Aware Model
Abhishek Setty, Lukas Morand, Poojitha Ramachandra +1
This study explores the applicability of a graph-based interaction-aware trajectory prediction model, originally developed for the transportation domain, to forecast particle traje…
Semantic orchestration and exploitation of material data: A dataspace solution demonstrated on steel and copper applications
Yoav Nahshon, Lukas Morand, Matthias Büschelberger +5
In materials science and manufacturing, vast amounts of heterogeneous data (e.g., measurement and simulation logs, process data, publications) serve as the bedrock of valuable know…
Accurate Distances Measures and Machine Learning of the Texture-Property Relation for Crystallographic Textures Represented by One-Point Statistics
Tarek Iraki, Lukas Morand, Norbert Link +2
The crystallographic texture of metallic materials is a key microstructural feature that is responsible for the anisotropic behavior, e.g., important in forming operations. In mate…
Machine learning for structure-guided materials and process design
Lukas Morand, Tarek Iraki, Johannes Dornheim +3
In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to ta…
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…
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…
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…
Compensating data shortages in manufacturing with monotonicity knowledge
Martin von Kurnatowski, Jochen Schmid, Patrick Link +5
Optimization in engineering requires appropriate models. In this article, a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the fo…
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…
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…
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…