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Fundamental Algorithms/Applied Math


Numerical simulation of real-world phenomena provides fertile ground for building interdisciplinary relationships. The SCI Institute has a long tradition of building these relationships in a win-win fashion – a win for the theoretical and algorithmic development of numerical modeling and simulation techniques and a win for the discipline-specific science of interest. High-order and adaptive methods, uncertainty quantification, complexity analysis, and parallelization are just some of the topics being investigated by SCI faculty. These areas of computing are being applied to a wide variety of engineering applications ranging from fluid mechanics and solid mechanics to bioelectricity.

Martin Berzins
Martin Berzins
– Parallel Computing
– GPUs
Profile
Mike Kirby
Mike Kirby
– Finite Element Methods
– Uncertainty Quantification
– GPUs
Profile
Akil Narayan
Akil Narayan
– Approximation theory and methods
– Sparse and regularized representations
– Mathematical shape analysis
– High-order numerical methods
– Data assimilation
Profile
Bao Wang
Bao Wang
– Data science
– Deep learning
– Stochastic optimization
– Large scale scientific computing
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