2023/04/24 by Nikita Belokonev, Belokonev, Nikita, Artem Melnikov +9 · 1 citation
Computer Science · Materials Science · Mathematics · #Advanced Multi-Objective Optimization Algorithms #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn) #Machine Learning in Materials Science #Quantum Physics (quant-ph) #Tensor decomposition and applications
paper · pdf · doi:10.48550/arxiv.2304.12307
openalex publication_date 2023/04/24 · openalex created_date 2023/04/27 · openalex updated_date 2026/07/28
Chemical component design is a computationally challenging procedure that often entails iterative numerical modeling and authentic experimental testing. We demonstrate a novel optimization method, Tensor train Optimization (TetraOpt), for the shape optimization of components focusing on a Y-shaped mixer of fluids. Due to its high parallelization and more extensive global search, TetraOpt outperforms commonly used Bayesian optimization techniques in accuracy and runtime. Besides, our approach can be used to solve general physical design problems and has linear complexity in the number of optimized parameters, which is highly relevant for complex chemical components. Furthermore, we discuss the extension of this approach to quantum computing, which potentially yields a more efficient approach.