5 papers
Bayesian Optimization of Genetic Algorithm Hyperparameters in a Multi-Fidelity Framework for Efficient Lattice Material Design
Sergei Zorkaltsev, Maciej Haranczyk, Christina Schenk
This study presents a multi-fidelity framework for the systematic optimization of genetic algorithm (GA) hyperparameters. The framework integrates three fidelity levels: high-fidel…
Noise-Aware Optimization in Nominally Identical Manufacturing and Measuring Systems for High-Throughput Parallel Workflows
Christina Schenk, Miguel Hernández-del-Valle, Luis Calero-Lumbreras +2
Device-to-device variability in experimental noise critically impacts reproducibility, especially in automated, high-throughput systems like additive manufacturing farms. While man…
A Novel Constrained Sampling Method for Efficient Exploration in Materials and Chemical Mixture Design
Christina Schenk, Maciej Haranczyk
Efficient exploration of multicomponent material composition spaces is often limited by time and financial constraints, particularly when mixture and synthesis constraints exist. T…
Model-Based Reinforcement Learning Control of Reaction-Diffusion Problems
Christina Schenk, Aditya Vasudevan, Maciej Haranczyk +1
Mathematical and computational tools have proven to be reliable in decision-making processes. In recent times, in particular, machine learning-based methods are becoming increasing…
tda-segmentor: A tool to extract and analyze local structure and porosity features in porous materials
Aditya Vasudevan, Jorge Zorrilla Prieto, Sergei Zorkaltsev +1
Local geometrical features of a porous material such as the shape and size of a pore or the curvature of a solid ligament often affect the macroscopic properties of the material, a…