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Improving control based importance sampling strategies for metastable diffusions via adapted metadynamics

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  • Sampling rare events in metastable dynamical systems is often a computationally expensive task and one needs to resort to enhanced sampling methods such as importance sampling. Since we can formulate the problem of finding optimal importance sampling controls as a stochastic optimization problem, this then brings additional numerical challenges and the convergence of corresponding algorithms might as well suffer from metastabilty. In this article we address this issue by combining systematic control approaches with the heuristic adaptive metadynamics method. Crucially, we approximate the importance sampling control by a neural network, which makes the algorithm in principle feasible for high dimensional applications. We can numerically demonstrate in relevant metastable problems that our algorithm is more effective than previous attempts and that only the combination of the two approaches leads to a satisfying convergence and therefore to an efficient sampling in certain metastable settings.
Author:Enric Ribera BorrellORCiD, Jannes Quer, Lorenz RichterORCiD, Christof Schütte
Document Type:Article
Parent Title (English):SIAM Journal on Scientific Computing (SISC)
First Page:S298
Last Page:S323
Tag:importance sampling; metadynamics; metastability; neural networks; rare event simulation; stochastic optimal control
Year of first publication:2023
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