6 Result(s)
within
Lukas Morand
English
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Article
Open AccessA multi-task learning-based optimization approach for finding diverse sets of microstructures with desired properties
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 to manufacture workpieces with tar...
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Article
Open AccessNeural Networks for Constitutive Modeling: From Universal Function Approximators to Advanced Models and the Integration of Physics
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 behavior plays a central role. C...
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Article
Open AccessOptimizing machine learning yield functions using query-by-committee for support vector classification with a dynamic stop** criterion
In the field of materials engineering, the accurate prediction of material behavior under various loading conditions is crucial. Machine Learning (ML) methods have emerged as promising tools for generating con...
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Article
Open AccessDeep reinforcement learning methods for structure-guided processing path optimization
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 paper, we propose and investigat...
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Chapter and Conference Paper
CupNet – Pruning a Network for Geometric Data
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 in a straightforward way.
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Chapter and Conference Paper
The Good, the Bad and the Ugly: Augmenting a Black-Box Model with Expert Knowledge
We address a non-unique parameter fitting problem in the context of material science
