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# Scientific Computing, Machine Learning and Analysis of PDE

## Our Research

SFU has award-winning research faculty whose interests lie in the interplay between scientific computing, approximation theory, machine learning and analysis of partial differential equations (PDE). Some of today’s richest mathematical questions and most impactful algorithms arise in this vibrant field.

One of the largest such groups in Canada, our mathematics research includes: analysis of PDE and integral equations, PDEs on surfaces, spectral analysis, foundations of data science, mathematics of machine learning, numerical analysis, mathematical modelling, compressed sensing and sparse recovery. The models and applications we work on range from weather and climate, plasma physics, muscle mechanics, image and signal processing, graphics, optimal design and self-collective behaviour.

This is a fast-moving field, where advanced ideas from computing, machine learning and analysis enhance and inform our understanding of some of the gnarliest continuum models out there.

We haven’t met a challenging continuum model we’re afraid of, yet.

## People

### Faculty

**Ben Adcock**

Numerical Analysis, Applied and Computational Harmonic Analysis, Compressed Sensing

**Razvan Fetecau**

Applied PDE Analysis, Self-Organizing Behaviour

**David Muraki**

Geophysical Fluid Dynamics, Asymptotic Methods

**Nilima Nigam**

Numerical Analysis, Spectral Methods, Mathematical Biology

**Steve Ruuth**

Scientific Computing, PDE's On Surfaces

**John Stockie**

Computational Fluid Dynamics, Industrial Mathematics

**Weiran Sun**

Applied PDE Analysis, Kinetic Theory

**Manfred Trummer**

Scientific Computing, Medical Imaging