Yuting An 
- (2023): Energy-efficient control of indoor PM2.5 and thermal comfort in a real room using deep reinforcement learning. In: Energy and Buildings, v. 295 (September 2023).
- (2023): Predicting spatial distribution of ultraviolet irradiance and disinfection of exhaled bioaerosols with a modified irradiance model. In: Building and Environment, v. 228 (Januar 2023).
- (2022): Smart control of window and air cleaner for mitigating indoor PM2.5 with reduced energy consumption based on deep reinforcement learning. In: Building and Environment, v. 224 (Oktober 2022).
- (2022): A combined deep learning and physical modelling method for estimating air pollutants’ source location and emission profile in street canyons. In: Building and Environment, v. 219 (Juli 2022).
- (2022): Predicting transient particle transport in periodic ventilation using Markov chain model with pre-stored transition probabilities. In: Building and Environment, v. 211 (März 2022).
- (2021): Exploring the feasibility of predicting contaminant transport using a stand-alone Markov chain solver based on measured airflow in enclosed environments. In: Building and Environment, v. 202 (September 2021).
- (2021): A reinforcement learning approach for control of window behavior to reduce indoor PM2.5 concentrations in naturally ventilated buildings. In: Building and Environment, v. 200 (August 2021).
- (2020): Modeling transient particle transport in transient indoor airflow by fast fluid dynamics with the Markov chain method. In: Building and Environment, v. 186 (Dezember 2020).
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