Deep Learning Surrogates for Real-Time Gas Emission Inversion

Download

• arXiv

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.

Citation

Newman, T., Nemeth, C., Jones, M., & Jonathan, P. (2025). Deep Learning Surrogates for Real-Time Gas Emission Inversion. arXiv preprint.

@article{newman2025deep,
  title={Deep Learning Surrogates for Real-Time Gas Emission Inversion},
  author={Newman, Thomas and Nemeth, Christopher and Jones, Matthew and Jonathan, Philip},
  journal={arXiv preprint arXiv:2506.14597},
  year={2025}
}