2 papers
math.NA2024
Reduced-order modelling based on Koopman operator theory
Diana A. Bistrian, Gabriel Dimitriu, Ionel M. Navon
The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output u…
cs.LG2024
Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)
Shengjuan Cai, Fangxin Fang, Vincent-Henri Peuch +3
PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to…