Project Overview#

From modeling population dynamics to understanding the formation of stars, PDE permeate the world of science and engineering. For most real-world problems, the lack of closed-form solutions requires computationally expensive numerical solvers, sometimes consuming millions of core hours and terabytes of storage.

This project aims to identify the conceptual and mathematical relationship among a few different types of neural representation for PDEs, on regular grids or irregular geometries, and design machine learning models that could solve Naivier Stokes equations efficiently and accurately.

  2. Tran, Alasdair, A. Mathews, Lexing Xie and Cheng Soon Ong. “Factorized Fourier Neural Operators.” (2021)


  • Solid knowledge of machine learning or time series models, e.g. COMP4670/8600 or equivalent.
  • Comfortable prototyping machine learning algorithms or simulation, python or R.
  • Strong ability to critically examine mathematical or empirical results.
  • Able to communicate technical ideas clearly, and work effectively in a research team.


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