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Center for Nonlinear Studies |
We have developed a rigorous mathematical framework for quantifying uncertainty (OUQ), and a robust software (mystic) that lowers the barrier to solving complex problems in predictive science. In OUQ, calculations of uncertainty, risk, probability of failure, certification, and experiment design are formulated as global optimizations over all possible valid scenarios. Such optimizations, however, are high-dimensional, highly-constrained, and non-convex, and generally impossible to solve with current optimization technology. Mystic is built to rigorously solve high-dimensional non-convex optimization problems with highly nonlinear complex constraints, is capable of solving global optimization problems with thousands of parameters and thousands of constraints, and makes it almost trivial to leverage high-performance parallel computing. Mystic and OUQ have been used in calculations of materials failure under hypervelocity impact, elasto-plastic failure in structures under seismic ground acceleration, phonon anharmonicity in semiconductor design, and risk in financial portfolios. I will present an overview of our mathematical framework, and the powerful optimization software we have developed to solve these types of large-scale optimization problems.
Mystic is open source, and available at https://pypi.python.org/pypi/mystic.
Host: Tim Germann