Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …

Abstract Machine learning studies need colossal power to process massive datasets and
train neural networks to reach high accuracies, which have become gradually …

With recent advances in building foundation models for texts and video data, there is a surge
of interest in foundation models for time series. A family of models have been developed …

Abstract The McKean-Vlasov equation (MVE) describes the collective behavior of particles
subject to drift, diffusion, and mean-field interaction. In physical systems, the interaction term …

Extreme Learning Machine (ELM) is becoming a popular learning algorithm due to its
diverse applications, including Human Activity Recognition (HAR). In ELM, the hidden node …

Physical simulations of fluids are crucial for understanding fluid dynamics across many
applications, such as weather prediction and engineering design. While high-resolution …

Solving partial differential equations (PDEs) efficiently is essential for analyzing complex
physical systems. Recent advancements in leveraging deep learning for solving PDE have …

Elliptic partial differential equations (PDEs) are a major class of time-independent PDEs that
play a key role in many scientific and engineering domains such as fluid dynamics, plasma …

Physical simulations are essential tools across critical fields such as mechanical and
aerospace engineering, chemistry, meteorology, etc. While neural operators, particularly the …

Epilepsy is a chronic brain disease characterized by recurrent and transient seizures, which
is accompanied by super-synchronous abnormal discharge of electroencephalogram (EEG) …