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Fundamentals

fundamentals.jpgComputational work in natural sciences relies on advances in numerical mathematics, algorithms and computer science. Examples: partial differential equations (PDEs); new strategies for parallel computation; algorithmic approaches to high-performance computing

People:

Barkley, Chung, Elliott, Jarvis, Kerr, Stuart, Icardi

Projects:

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Computational Engineering and Fluids

cef.jpgComputational engineering supports almost all branches of traditional engineering. Predicting what will happen, quantitatively, when fluids and gases flow, often with added complications such as: simultaneous flow of heat; mass transfer; chemical reaction; mechanical movement; stresses, etc.

People:

Arber, Chung, Barkley, Kerr, Icardi

Projects:

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Monte Carlo and Stochastic Methods

MCSS imageMonte Carlo methods provide approximate solutions to quantitative problems by inferring from samples produced through stochastic simulation. While the method itself is based on statistical simulation the problems solved can be both deterministic or probabilistic. A very popular Monte Carlo method is based on Markov chains and known as MCMC (Markov chain Monte Carlo).

People:

Quigley, Roemer, Stuart

Projects:

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Computation of Living Systems

CLS imageBiology as a quantitative science is more and more relying on large-scale computational approaches to understand the complex behaviour of living systems. This starts with investigations at the molecular level, continues to models of proteins, bio-polymers, cells and their dynamics and culminates in the simulation of whole habitats.

People:

Elliott, Feng, Kirkilionis, Roemer

Projects:

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