Particle Dispersion in Indoor Environments: Can Super-resolution Autoencoders Revolutionize Air Quality Predictions?

The critical concern of airborne infections and their associated health implications has underscored the necessity for a comprehensive understanding of indoor airflow dynamics and particle dispersion. While the conventional use of Computational Fluid Dynamics (CFD) has been instrumental in indoor air quality assessments, the repeated analysis of varying patient postures may prove time-consuming. To address this issue and improve efficiency, there exists an opportunity to develop a Super-resolution Autoencoder (SR-AE) algorithm for the sake of the prediction purposes. By employing the SR-AE algorithm and snapshots obtained from the high-fidelity CFD simulations, providing direct visualization of particle dispersion in indoor environments, this study aims to demonstrate the potential of a novel Autoencoder-based approach in accurately interpreting indoor airflow and particle dynamics for airborne infection exposure assessment. Through this research, we are seeking to bridge the gap between high-fidelity CFD simulations and advanced deep learning techniques, contributing to more efficient indoor air quality predictions, thus mitigating health risks and ensuring safer indoor environments.