Predicting the spatio-temporal distribution of the droplets based on the machine learning algorithm
Bibliographic record
Abstract
Aerosol pollutants composed of suspended droplets significantly impact environmental quality and human health. Predicting the spatiotemporal distribution of cough droplets remains a challenge due to their complex multiphase dynamics, involving intricate interactions between droplet motion and turbulent airflow. This study presents a three-dimensional Gaussian parameter model integrating computational fluid dynamics (CFD) with machine learning to efficiently simulate and predict the transport and dispersion of indoor cough droplets. The Gaussian model derived from CFD flow field dynamics and droplet kinematics adheres to conservation principles and hyperbolicity, ensuring physical consistency. An adaptive polynomial feature random forest algorithm predicts model parameters, enabling rapid reconstruction of droplet trajectories and spatial distribution patterns. The approach achieves a 76.4% reduction in computational cost compared to traditional CFD simulations while maintaining high accuracy, with a mean absolute error below 0.07 and a mean squared error below 0.014. This robust and versatile framework advances the understanding of aerosol transport dynamics, offering critical insight and practical tools for indoor air quality management and aerosol pollution control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".