### Refine

#### Document Type

- Article (4)
- ZIB-Report (4)
- Doctoral Thesis (1)

#### Language

- English (9)

#### Is part of the Bibliography

- no (9)

#### Keywords

- importance sampling (2)
- metadynamics (2)
- metastability (2)
- neural networks (2)
- rare event simulation (2)
- stochastic optimal control (2)
- Adaptive Importance Sampling (1)
- Girsanov (1)
- Metadynamics (1)
- Metastability (1)

#### Institute

Some connections between importance sampling and enhanced sampling methods in molecular dynamics
(2017)

Enhanced sampling methods play an important role in molecular dynamics, because they enable the collection of better statistics of rare events that are important in many physical phenomena. We show that many enhanced sampling methods can be viewed as methods for performing importance sampling, by identifying important correspondences between the language of molecular dynamics and the language of probability theory. We illustrate these connections by highlighting the similarities between the rare event simulation method of Hartmann and Schütte (J. Stat. Mech. Theor. Exp., 2012), and the enhanced sampling method of Valsson and Parrinello (Phys. Rev. Lett. 113, 090601). We show that the idea of changing a probability measure is fundamental to both enhanced sampling and importance sampling.

An automatic adaptive importance sampling algorithm for molecular dynamics in reaction coordinates
(2017)

In this article we propose an adaptive importance sampling scheme for dynamical quantities of high dimensional complex systems which are metastable. The main idea of this article is to combine a method coming from Molecular Dynamics Simulation, Metadynamics, with a theorem from stochastic analysis, Girsanov's theorem. The proposed algorithm has two advantages compared to a standard estimator of dynamic quantities: firstly, it is possible to produce estimators with a lower variance and, secondly, we can speed up the sampling. One of the main problems for building importance sampling schemes for metastable systems is to find the metastable region in order to manipulate the potential accordingly. Our method circumvents this problem by using an assimilated version of the Metadynamics algorithm and thus creates a non-equilibrium dynamics which is used to sample the equilibrium quantities.

In this article we present a new idea for approximating exit rates for diffusion processes living in a craggy landscape. We are especially interested in the exit rates of a process living in a metastable regions. Due to the fact that Monte Carlo simulations perform quite poor and are very computational expensive in this setting we create several similar situations with a smoothed potential. For this we introduce a new parameter $\lambda \in [0,1]$ ($\lambda = 1$ very smoothed potential, $\lambda=0$ original potential) into the potential which controls the influence the smoothing. We then sample the exit rate for different parameters $\lambda$ the exit rate from a given region. Due to the fact that $\lambda$ is connected to the exit rate we can use this dependency to approximate the real exit rate. The method can be seen as something between hyperdynamics and temperature accelerated MC.

An automatic adaptive importance sampling algorithm for molecular dynamics in reaction coordinates
(2018)

In this article we study the connection of stochastic optimal control and reinforcement learning. Our main motivation is an importance sampling application to rare events sampling which can be reformulated as an optimal control problem. By using a parameterized approach the optimal control problem turns into a stochastic optimization problem which still presents some open questions regarding how to tackle the scalability to high-dimensional problems and how to deal with the intrinsic metastability of the system. With the aim to explore new methods we connect the optimal control problem to reinforcement learning since both share the same underlying framework namely a Markov decision process (MDP). We show how the MDP can be formulated for the optimal control problem. Furthermore, we discuss how the stochastic optimal control problem can be interpreted in a reinforcement learning framework. At the end of the article we present the application of two different reinforcement learning algorithms to the optimal control problem and compare the advantages and disadvantages of the two algorithms.

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.

Some connections between importance sampling and enhanced sampling methods in molecular dynamics
(2017)