用可微物理神经模型加速粗粒化物理模拟，快100倍且保持高精度
Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
- 融合物理模型与神经网络，联合学习各向异性扩散系数和非马尔可夫闭包
- 仅用26组数据训练，实现小时级仿真压缩至1分钟内完成
- 适用于分布外场景，对移动源任务仍保持0.96的高相关性
数值模拟为诸多物理现实问题提供关键洞见。尽管这些模拟在完整3D域上求解，但多数分析仅需有限度的指标（如平面浓度）。本文提出一种混合物理-神经模型，可在复杂域中以比3D模拟快数个数量级的速度预测标量输运（从数小时缩短至不足1分钟）。该端到端可微框架联合学习物理模型参数化（即各向异性扩散率）与非马尔可夫神经闭包模型，以捕捉未解析的粗粒化效应，从而支持稳定、长时间滚动推演。该模型数据效率高（仅需26组训练数据），并可灵活扩展至分布外场景（含移动源），在最终仿真时刻达到0.96的斯皮尔曼相关系数。整体结果表明，该可微物理-神经框架可实现快速、准确且泛化性强的物理现象粗粒化代理模型。
原文摘要 · Abstract (English)
Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a reduced set of metrics (e.g. plane-level concentrations). This work presents a hybrid physics-neural model that predicts scalar transport in a complex domain orders of magnitude faster than the 3D simulation (from hours to less than 1 min). This end-to-end differentiable framework jointly learns the physical model parameterization (i.e. orthotropic diffusivity) and a non-Markovian neural closure model to capture unresolved, 'coarse-grained' effects, thereby enabling stable, long time horizon rollouts. This proposed model is data-efficient (learning with 26 training data), and can be flexibly extended to an out-of-distribution scenario (with a moving source), achieving a Spearman correlation coefficient of 0.96 at the final simulation time. Overall results show that this differentiable physics-neural framework enables fast, accurate, and generalizable coarse-grained surrogates for physical phenomena.
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