Work

Optimal Control of Material Micro-Structures

Year: 2022

Type: preprint

Abstract: In this paper, we consider the optimal control of material micro-structures. Such material micro-structures are modeled by the so-called phase field model. We study the underlying physical structure of the model and propose a data based approach for its optimal control, along with a comparison to the control using a state of the art Reinforcement Learning (RL) algorithm. Simulation results show the feasibility of optimally controlling such micro-structures to attain desired material properties and complex target micro-structures.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2210.06734

Authors: Sharma, Aayushman, Mao, Zirui, Yang, Haiying, Chakravorty, Suman, Demkowicz, Michael J, Kalathil, Dileep

Language: English

Topic: Solidification and crystal growth phenomena

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Award: CDS&E: Optimal control of material microstructure evolution via massively parallel computing

OpenAlex ID: W4306295147

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