Research Interests

  • Scientific Machine Learning: Neural Operators, Inverse Problems.
  • Numerical Methods for Partial Differential Equations: finite element methods, iterative solvers.
  • Computational Electromagnetics.
  • Computational Fluid Dynamics. 
EIT prediction

Deep Learning \(\times\) Inverse Problems

Study how to use neural operator to enhance non-iterative methods (such as direct sampling) for inverse problems in the regime of limited data.

We are interested particularly in the Calderón problem.

Neural Operator-Assisted Numerical PDE Solver

Leveraging neural operators' representation capacity to enhance classical numerical schemes, such as finite volume methods or finite element methods.