Speaker
Description
Biomolecular processes often involve transitions between metastable states governing molecular function. Extracting this information is challenging due to the high dimensionality of simulation data. Kernel methods are a powerful tool for automated analysis of complex systems, yet their need for pairwise kernel evaluations leads to scalability issues, particularly in large datasets.
To address this, we integrate Random Fourier Features (RFF) into Kernel-based Extended Dynamic Mode Decomposition (EDMD), providing a scalable method for studying long-time kinetics in biomolecular systems. By comparing and combining this approach with other dimensionality reduction techniques, we demonstrate its robustness and potential for enabling efficient analysis of molecular data.
