用深度学习替代耗时的流体模拟，实现气体排放的实时定位与量化。
Deep Learning Surrogates for Real-Time Gas Emission Inversion
- 用神经网络替代计算流体模拟，加速气体扩散预测。
- 在甲烷释放数据集上精度接近全量模拟，速度提升数百倍。
- 适合工业排放监控等需快速响应的环境建模任务。
在瞬变大气条件下实时识别和量化温室气体排放是环境监测的关键挑战。本文提出一种时空反演框架，将基于计算流体动力学（CFD）输出训练的深度学习代理模型嵌入序贯蒙特卡洛算法中，实现对动态流场下排放速率和源位置的贝叶斯推断。通过用多层感知机替代高成本数值求解器，该代理模型能捕捉气体扩散的空间异质性和时间演化特性，并实现近实时预测。在Chilbolton甲烷释放数据集上的验证表明，其精度可与完整CFD求解器和高斯烟羽模型相媲美，同时运行速度提升数个数量级。在模拟障碍物干扰场景下的进一步实验也证实了其在复杂环境中的鲁棒性。本工作实现了物理保真度与计算可行性之间的平衡，为工业排放监测及其他时间敏感的时空反演任务提供了可扩展的解决方案。
原文摘要 · Abstract (English)
Real-time identification and quantification of greenhouse-gas emissions under transient atmospheric conditions is a critical challenge in environmental monitoring. We introduce a spatio-temporal inversion framework that embeds a deep-learning surrogate of computational fluid dynamics (CFD) within a sequential Monte Carlo algorithm to perform Bayesian inference of both emission rate and source location in dynamic flow fields. By substituting costly numerical solvers with a multilayer perceptron trained on high-fidelity CFD outputs, our surrogate captures spatial heterogeneity and temporal evolution of gas dispersion, while delivering near-real-time predictions. Validation on the Chilbolton methane release dataset demonstrates comparable accuracy to full CFD solvers and Gaussian plume models, yet achieves orders-of-magnitude faster runtimes. Further experiments under simulated obstructed-flow scenarios confirm robustness in complex environments. This work reconciles physical fidelity with computational feasibility, offering a scalable solution for industrial emissions monitoring and other time-sensitive spatio-temporal inversion tasks in environmental and scientific modeling.
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