学习连续性的关闭,以控制相互作用的活性粒子
Titus Quah1, Sho C Takatori2, James B Rawlings1
1Department of Chemical Engineering, University of California, Santa Barbara, California 93106, USA.
The Journal of chemical physics
|January 30, 2026
概括
这项研究引入了一种新的学习框架,通过学习连续模型来控制活性物质群. 这种方法可以精确操纵粒子密度和流量,为可编程材料铺平了道路.
科学领域:
- 物理 物理学 物理
- 材料科学 材料科学 材料科学
- 控制理论 控制理论
背景情况:
- 活体物质群很难控制,尤其是在拥挤的系统中,在这些系统中,单个代理控制是不切实际的.
- 现有的控制方法需要快速,准确和可区分的模型,这些模型对于复杂的活性物质动态来说是具有挑战性的.
研究的目的:
- 开发一个学习控制的框架,以利用宏观场来控制活体物质群.
- 为了应对构建准确连续闭合的挑战,为活性物质动态.
主要方法:
- 采用了通用微分方程 (UDE) 框架,将连续体表示为向向-扩散方程.
- 一个神经运算符被用来学习向导术语,为显微效应提供闭合关系.
- 学习的连续模型被集成到模型预测控制 (MPC) 中,用于代理模拟控制.
主要成果:
- 该框架成功地从活性布朗粒子的代理模拟中学习了连续性闭合.
- 通过在组之间动态交换粒子密度来证明精确的控制.
- 实现了同时控制颗粒密度和平均流量以遵循侧面形状.
结论:
- 基于UDE的学习控制框架提供了一种强大的方法来控制活体物质群.
- 这种方法有助于开发具有可控制动态性质的可编程材料.
- 该框架确保遵守物理定律,同时从数据中学习复杂的动态.
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