Neural-PDE: a RNN based neural network for solving time dependent PDEs

Shixin Xu is an Assistant Professor of Mathematics whose research spans several dynamic and interconnected fields. His primary interests include machine learning and data-driven models for disease prediction, multiscale modeling of complex fluids, neurovascular coupling, homogenization theory, and numerical analysis. His current projects reflect a diverse and impactful portfolio:

• Developing predictive models based on image data to identify hemorrhagic transformation in acute ischemic stroke.
• Conducting electrodynamics modeling of saltatory conduction along myelinated axons to understand nerve impulse transmission.
• Engaging in electrochemical modeling to explore the interactions between electric fields and chemical processes.
• Investigating fluid-structure interactions with mass transport and reactions, crucial for understanding physiological and engineering systems.

These projects demonstrate his commitment to addressing complex problems through interdisciplinary approaches that bridge mathematics with biological and physical sciences.