Optimal Control of Material Micro-Structures

TL;DR

This paper presents a data-driven optimal control approach for material micro-structures modeled by phase field models, comparing it with reinforcement learning, and demonstrating its effectiveness through simulations.

Contribution

It introduces a novel data-based control method for phase field models and compares its performance with reinforcement learning techniques.

Findings

The proposed control method effectively attains desired micro-structures.

Simulation results validate the feasibility of controlling material properties.

Comparison shows advantages over existing RL approaches.

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.