Building a Framework for Predictive Science
Key questions that scientists and engineers typically want to address can be formulated in terms of predictive science. Questions such as: "How well does my computational model represent reality?", "What are the most important parameters in the problem?", and "What is the be...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
06.02.2012
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Subjects | |
Online Access | Get full text |
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Summary: | Key questions that scientists and engineers typically want to address can be
formulated in terms of predictive science. Questions such as: "How well does my
computational model represent reality?", "What are the most important
parameters in the problem?", and "What is the best next experiment to perform?"
are fundamental in solving scientific problems. Mystic is a framework for
massively-parallel optimization and rigorous sensitivity analysis that enables
these motivating questions to be addressed quantitatively as global
optimization problems. Often realistic physics, engineering, and materials
models may have hundreds of input parameters, hundreds of constraints, and may
require execution times of seconds or longer. In more extreme cases, realistic
models may be multi-scale, and require the use of high-performance computing
clusters for their evaluation. Predictive calculations, formulated as a global
optimization over a potential surface in design parameter space, may require an
already prohibitively large simulation to be performed hundreds, if not
thousands, of times. The need to prepare, schedule, and monitor thousands of
model evaluations, and dynamically explore and analyze results, is a
challenging problem that requires a software infrastructure capable of
distributing and managing computations on large-scale heterogeneous resources.
In this paper, we present the design behind an optimization framework, and also
a framework for heterogeneous computing, that when utilized together, can make
computationally intractable sensitivity and optimization problems much more
tractable. |
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DOI: | 10.48550/arxiv.1202.1056 |