47 citations · 83 across the 7 of their papers we have counts for
7 papers
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…
gTLO: A Generalized and Non-linear Multi-Objective Deep Reinforcement Learning Approach
Johannes Dornheim
In real-world decision optimization, often multiple competing objectives must be taken into account. Following classical reinforcement learning, these objectives have to be combine…
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…
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…
Multiobjective Reinforcement Learning for Reconfigurable Adaptive Optimal Control of Manufacturing Processes
Johannes Dornheim, Norbert Link
In industrial applications of adaptive optimal control often multiple contrary objectives have to be considered. The weights (relative importance) of the objectives are often not k…
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement Learning
Johannes Dornheim, Norbert Link, Peter Gumbsch
A self-learning optimal control algorithm for episodic fixed-horizon manufacturing processes with time-discrete control actions is proposed and evaluated on a simulated deep drawin…