Diffusion for Diffusion: A versatile multiphysics fields refinement framework in pollutants transportation.

Area of Science:

• Environmental Science
• Computational Fluid Dynamics
• Machine Learning

Background:

• Accurate pollutant transport modeling is crucial for environmental risk assessment and industrial pollution control.
• Current numerical solvers and machine learning methods face challenges in computational efficiency, generalizability, and prediction accuracy.

Purpose of the Study:

• To develop a novel deep learning framework for refining coupled multiphysics fields in pollutant transport using sparse measurements.
• To address the limitations of existing methods in terms of efficiency, generalizability, and accuracy.

Main Methods:

• A physics-informed, equation-constrained deep learning framework based on conditional diffusion models.
• Application to pollutant diffusion driven by coupled concentration and temperature gradients in 2D transient and 3D steady-state scenarios.
• Validation using benchmark datasets from finite element simulations and double-color siren imaging.

Main Results:

• The proposed diffusion-model framework accurately recovers high-dimensional spatiotemporal pollutant fields from limited observations.
• Consistent outperformance of state-of-the-art baselines (ResNet, Transformer, Fourier Neural Operator) in super-resolution accuracy and robustness.
• Demonstrated effectiveness across varied source distributions and boundary conditions.

Conclusions:

• The developed diffusion-model deep learning approach offers a significant advancement in pollutant transport field reconstruction.
• This method provides a promising pathway for real-time monitoring and analysis in environmental and industrial pollution contexts.
• The framework shows potential for improving environmental risk assessment and pollution mitigation strategies.