6248
2016
eng
2298
2326
8
29
article
0
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Model reduction algorithms for optimal control and importance sampling of diffusions
We propose numerical algorithms for solving optimal control and importance sampling problems based on simplified models. The algorithms combine model reduction techniques for multiscale diffusions and stochastic optimization tools, with the aim of reducing the original, possibly high-dimensional problem to a lower dimensional representation of the dynamics, in which only a few relevant degrees of freedom are controlled or biased. Specifically, we study situations in which either a reaction coordinate onto which the dynamics can be projected is known, or situations in which the dynamics shows strongly localized behavior in the small noise regime. No explicit assumptions about small parameters or scale separation have to be made. We illustrate the approach with simple, but paradigmatic numerical examples.
Nonlinearity
10.1088/0951-7715/29/8/2298
yes
Carsten Hartmann
Erlinda Körnig
Christof Schütte
Wei Zhang
Numerical Mathematics
Schütte, Christof
SFB1114-A5