55 Papers
316 Citations
Mathias Rousset is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Rare events & Stochastic process. The author has an hindex of 16, co-authored 50 publications. Previous affiliations of Mathias Rousset include university of lille & École des ponts ParisTech.
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Papers
Computation of free energy profiles with parallel adaptive dynamics.
TL;DR: A formulation of an adaptive computation of free energy differences, in the adaptive biasing force or nonequilibrium metadynamics spirit, using conditional distributions of samples of configurations which evolve in time, to present a truly unifying framework for these methods and to prove convergence results for certain classes of algorithms.
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Langevin dynamics with constraints and computation of free energy differences
TL;DR: A consistent discretization of the overdamped Langevin (Brownian) dynamics on a submanifold is obtained, also sampling exactly the correct canonical measure with constraints, in some limiting regime.
On the Control of an Interacting Particle Estimation of Schrödinger Ground States
TL;DR: A general Schrodinger operator L+V on a domain $E\subset {\mathbb R}^{d}$ and its associated positive ground state h solution to the maximal eigenvalue problem L(h) +Vh=\lambda h is considered.
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Langevin dynamics with constraints and computation of free energy differences
TL;DR: In this article, a simple discretization of the constrained Langevin process based on a standard splitting strategy was proposed, and the corresponding numerical methods can be used to sample a probability measure supported by a submanifold.
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Unbiasedness of some generalized Adaptive Multilevel Splitting algorithms
TL;DR: This work introduces a generalization of the Adaptive Multilevel Splitting algorithm in the discrete time dynamic setting, namely when it is applied to sample rare events associated with paths of Markov chains, by interpreting the algorithm as a sequential sampler in path space.