Deep Learning Surrogates for Real-Time Gas Emission Inversion

• URL: http://arxiv.org/abs/2506.14597v1
• Date: Tue, 17 Jun 2025 15:03:21 GMT
• Title: Deep Learning Surrogates for Real-Time Gas Emission Inversion
• Authors: Thomas Newman, Christopher Nemeth, Matthew Jones, Philip Jonathan,
• Abstract summary: Realtime identification and quantification of greenhouse-gas emissions under transient atmospheric conditions is a critical challenge in environmental monitoring.<n>We introduce a deep-learning computational fluid dynamics (CFD) framework that embeds a Monte Carlo sequential algorithm to perform Bayesian inference of both emission rate and source location in dynamic flow fields.<n>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.
• Score: 6.14441531828484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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