深度学习在活性物质中的概率流和生产率
Nicholas M Boffi1, Eric Vanden-Eijnden1
1Courant Institute of Mathematical Sciences, New York University, New York, NY 10012.
概括
这项研究引入了一个深度学习框架,以有效计算活性物质系统中的关键指标,揭示粒子如何驱动不平衡状态. 该方法准确量化了复杂系统中的产量和概率电流,例如运动诱导相位分离.
科学领域:
- 统计力学 统计力学
- 活动物质物理学 活动物质物理学
- 机器学习应用 机器学习应用
背景情况:
- 活性物质系统将能量转化为工作,表现出超出平衡状态的统计力学之外的不平衡物理.
- 使用生成和概率流量量化不平衡状态的量化是具有挑战性的,因为它依赖于未知的概率密度.
- 这些指标的有效计算对于理解复杂的活性物质动态是至关重要的.
研究的目的:
- 开发一个深度学习框架,以有效估计活性物质系统中高维概率密度的得分.
- 能够从微观运动方程中准确计算产生率和概率电流.
- 将这些不平衡指标分解为单个粒子的局部贡献.
主要方法:
- 利用生成建模的进步来创建密度得分估计的深度学习框架.
- 引入了空间局部变压器网络架构,以学习粒子相互作用并保持对称性.
- 将框架应用于高维活性粒子系统,包括那些表现出动力诱导相分离 (MIPS) 的系统.
主要成果:
- 深度学习框架成功估计了得分,提供了对产量率和当前概率的访问.
- 该方法允许将这些量分解为局部粒子贡献.
- 一个训练有素的网络证明了MIPS系统中不同粒子数 (高达32,768) 和包装分数的概括性.
结论:
- 开发的深度学习方法提供了一种可扩展和有效的方法来量化活性物质中的不平衡现象.
- 该框架的概括能力突出了其在复杂物理系统中广泛应用的潜力.
- 这项工作为MIPS中不平衡偏离的空间结构提供了新的见解.
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