Efficient indoor environmental management is vital for reducing energy use while safeguarding indoor air quality and occupants’ comfort. However, due to difficulty of obtaining comprehensive datasets across multiple domains, many current studies still focus on a single performance metric, with dataset limitations preventing the development of robust and accurate multi-objective predictive models and operate as opaque data-driven systems with limited explanatory capability. This work developed CFD datasets derived from an experimentally validated model to systematically generate data covering parameters of indoor air quality, energy use, and thermal sensation with varied ventilation conditions. This dataset is further used for machine learning (ML) analysis. A cutting-edge Tabular Prior-data Fitted Network (TabPFN) is adopted for multi-target prediction and benchmarked against XGBoost, CatBoost, and Backpropagation Neural Networks (BPNN). SHapley Additive exPlanations (SHAP) method was used to elucidate its predictions, identifying how the most influential parameters govern the variations in indoor environmental behavior. The findings indicate that TabPFN outperforms the other three models in both predictive accuracy and computational efficiency, with MAE reduced by 62.08-95.4 % and RMSE by 49.15-85.50 %, while inference is accelerated by 67.5-85.9 %. SHAP analysis quantified nonlinear and directional contributions of features, linking model outputs to physical mechanisms such as draft risk, thermal sensation, and cooling load demand. The proposed TabPFN-SHAP framework is expected to offer valuable insights to guide the optimisation of building environmental control strategies.

Targeted drug delivery to the deep lung improves therapeutic outcomes, but respiratory system variability complicates drug spray design. Numerical simulations offer insights for individualized treatments but are computationally intensive, highlighting the need for surrogate models for real-time deposition prediction. This study comprehensively explores the multi-task predictive capability of regression models, including Linear regression (LR), Bayesian regression (BR), Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, and CatBoost, for predicting total and regional deposition rates of inhaled particles in airway. A training dataset is obtained from well-validated CFD simulations with realistic human airway model using Euler-Lagrangian method. The results indicate that LR, BR, and SVM yield unsatisfactory predictive accuracy, with average R² values in range of 0.21 to 0.73. Comparatively, BPNN and decisiontree-based models show great potential in predicting total deposition rate in the upper and central airway. However, for regional deposition rate prediction, BPNN did not consistently yield high accuracy, particularly for oral deposition (R² = 0.538). Comparatively, XGBoost emerges as optimal model, achieving an R² approximately close to 1 on both the training and testing datasets, with predictive errors within the range of ±0.5. The overall results demonstrate that decision-tree-based models, particularly XGBoost, have superior performance in accurately predicting both total and regional deposition rates of inhaled particles within airway. Despite limitations like geometry complexity and data quantity, the workflow developed in this study is expected to pave the way for future research integrating ML models into drug delivery device design and evaluation.