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Scientific Computing

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

Martin Berzins

Parallel Computing
GPUs
mike

Mike Kirby

Finite Element Methods
Uncertainty Quantification
GPUs
pascucci

Valerio Pascucci

Scientific Data Management
chris

Chris Johnson

Problem Solving Environments
ross

Ross Whitaker

GPUs
chuck

Chuck Hansen

GPUs
   

Scientific Computing Project Sites:


Publications in Scientific Computing:


Developing Software for Time-Dependent Problems Using the Method of Lines and Differential Algebraic Integrators
M. Berzins, P.M. Dew, R.M. Furzeland. In Applied Numerical Mathematics, Vol. 5, pp. 375--397. 1989.



A C1 Interpolant for Codes Based on Backward Differentiation Formulae
M. Berzins. In Applied Numerical Mathematics, Vol. 2, pp. 109--118. 1986.

This note is concerned with the provision of an interpolant for o.d.e. initial value codes based upon backward differentiation formulae (b.d.f.) in which both the solution and its first time derivative are continuous over the range of integration--a C1 interpolant. The construction and implementation of the interpolant is described and the continuity achieved in practice is illustrated by two examples.