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Found 6 papers, 1 papers with code
Machine learning for structure-guided materials and process design
The second is to solve a process design problem that is to find an optimal processing path to manufacture these material structures.
A multi-task learning-based optimization approach for finding diverse sets of material microstructures with desired properties and its application to texture optimization
The optimization along the chain processing-structure-properties-performance is one of the core objectives in data-driven materials science.
Compensating data shortages in manufacturing with monotonicity knowledge
In this article, a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints, is presented.
Deep Reinforcement Learning Methods for Structure-Guided Processing Path Optimization
Our proposed methods can find an optimal path from a start structure to a single target structure, or optimize the processing paths to one of the equivalent target-structures in the set.
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.
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.
