用机器学习从短时粒子模拟预测长时宏观动力学，还能量化不确定性。
SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty
- 结合统计物理与扩散模型，构建可量化认知不确定性的新框架。
- 仅用短时数据实现分钟级长时预测，计算效率提升数百倍。
- 适合需高可靠性热力学建模的复杂系统研究者使用。
基于数据发现具有粒子精度的耗散系统的长时间宏观动力学与热力学面临重大挑战，包括粒子模拟固有的时间尺度限制、从宏观动力学中反推热力学势和算子的非唯一性，以及高效不确定性量化的需求。本文提出统计物理感知的认知扩散模型（SPIEDiff），通过融合统计物理、条件扩散模型与epinets，克服这些局限。在随机阿伦尼乌斯粒子过程上的评估表明，SPIEDiff能准确恢复热力学与动力学特性，并仅依赖短时粒子模拟数据实现可靠的长时间宏观预测。相比直接粒子模拟需数天至数年，SPIEDiff可在分钟内完成准确预测并提供量化不确定性。整体上，SPIEDiff为热力学模型的数据驱动发现提供了稳健且可信的路径。
原文摘要 · Abstract (English)
The data-driven discovery of long-time macroscopic dynamics and thermodynamics of dissipative systems with particle fidelity is hampered by significant obstacles. These include the strong time-scale limitations inherent to particle simulations, the non-uniqueness of the thermodynamic potentials and operators from given macroscopic dynamics, and the need for efficient uncertainty quantification. This paper introduces Statistical-Physics Informed Epistemic Diffusion Models (SPIEDiff), a machine learning framework designed to overcome these limitations in the context of purely dissipative systems by leveraging statistical physics, conditional diffusion models, and epinets. We evaluate the proposed framework on stochastic Arrhenius particle processes and demonstrate that SPIEDiff can accurately uncover both thermodynamics and kinetics, while enabling reliable long-time macroscopic predictions using only short-time particle simulation data. SPIEDiff can deliver accurate predictions with quantified uncertainty in minutes, drastically reducing the computational demand compared to direct particle simulations, which would take days or years in the examples considered. Overall, SPIEDiff offers a robust and trustworthy pathway for the data-driven discovery of thermodynamic models.
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