Concept
Principe de grandes déviations
Publications associées (36)
LDP and CLT for SPDEs with transport noise
In this work we consider solutions to stochastic partial differential equations with transport noise, which are known to converge, in a suitable scaling limit, to solution of the corresponding deterministic PDE with an additional viscosity term. Large devi ...
SPRINGER2023Validation of D-T fusion power prediction capability against 2021 JET D-T experiments
Michele Marin, Javier García Hernández, Mikhail Maslov
JET experiments using the fuel mixture envisaged for fusion power plants, deuterium and tritium (D-T), provide a unique opportunity to validate existing D-T fusion power prediction capabilities in support of future device design and operation preparation. ...
Bristol2023The exploration process of critical Boltzmann planar maps decorated by a triangular O(n) loop model
In this paper we investigate pointed (q, g, n)-Boltzmann loop-decorated maps with loops traversing only inner triangular faces. Using peeling exploration Budd (2018) modified to this setting we show that its law in the non-generic critical phase can be cod ...
IMPA2022Gyrokinetic simulations of turbulence and zonal flows driven by steep profile gradients using a delta-f approach with an evolving background Maxwellian
Laurent Villard, Stephan Brunner, Alberto Bottino, Ben McMillan, Moahan Murugappan
Long global gyrokinetic turbulence simulations are particularly challenging in situations where the system deviates strongly from its initial state and when fluctuation levels are high, for example, in strong gradient regions. For particle-in-cell simulati ...
AIP Publishing2022Statistical limits of dictionary learning: Random matrix theory and the spectral replica method
Nicolas Macris, Jean François Emmanuel Barbier
We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...
AMER PHYSICAL SOC2022Distributionally Robust Optimization with Markovian Data
Daniel Kuhn, Mengmeng Li, Tobias Sutter
We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...
2021Work fluctuations in the active Ornstein-Uhlenbeck particle model
We study the large deviations of the power injected by the active force for an active Ornstein-Uhlenbeck particle (AOUP), free or in a confining potential. For the free-particle case, we compute the rate function analytically in d-dimensions from a saddle- ...
IOP Publishing Ltd2021Efficient Learning of a Linear Dynamical System with Stability Guarantees
Daniel Kuhn, Wouter Jongeneel, Tobias Sutter
We propose a principled method for projecting an arbitrary square matrix to the non- convex set of asymptotically stable matrices. Leveraging ideas from large deviations theory, we show that this projection is optimal in an information-theoretic sense and ...
2021A General Framework for Optimal Data-Driven Optimization
We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...
2021From Data to Decisions: Distributionally Robust Optimization is Optimal
Daniel Kuhn, Peyman Mohajerin Esfahani
We study stochastic programs where the decision-maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transfo ...
2020