Publications associées à Principe de grandes déviations

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 ...

SPRINGER2023

Validation 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. ...

IOP Publishing Ltd2023

The exploration process of critical Boltzmann planar maps decorated by a triangular O(n) loop model

Aleksandra Korzhenkova

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 ...

IMPA2022

Gyrokinetic 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, Moahan Murugappan, Alberto Bottino, Ben McMillan

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 ...

2022

Statistical 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 SOC2022

Distributionally Robust Optimization with Markovian Data

Daniel Kuhn, Tobias Sutter, Mengmeng Li

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 ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Work fluctuations in the active Ornstein-Uhlenbeck particle model

Francesco Cagnetta

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 Ltd2021

Efficient Learning of a Linear Dynamical System with Stability Guarantees

Daniel Kuhn, Tobias Sutter, Wouter Jongeneel

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 ...

2023

A Pareto Dominance Principle for Data-Driven Optimization

Daniel Kuhn, Tobias Sutter

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 ...

2024

Topological Linear System Identification via Moderate Deviations Theory

Daniel Kuhn, Tobias Sutter, Wouter Jongeneel

Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space. The induced equivalence classes capture qualitative properties such as stability or ...

2022

From 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 ...

2021

A moment-based model for plasma dynamics at arbitrary collisionality

Rogério Manuel Cabete De Jesus Jorge

Despite significant development over the last decades, a model able to describe the periphery region of magnetic confinement fusion devices, extending from the edge to the far scrape-off layer, is still missing. This is because this region is characterized ...

EPFL2019

Performance assessment of a 3-D steady-state and spatial kinetics model for the CROCUS reactor

Adolfo Rais

This dissertation covers both experimental and numerical neutronics studies to evaluate the adequacy of the Serpent/PARCS code sequence for modeling the steady-state and kinetics behavior of the CROCUS reactor. The reactor presents design characteristics t ...

EPFL2018

Deep Learning for Logic Optimization Algorithms

Giovanni De Micheli, Sabine Süsstrunk, Frédéric Kaplan, Mathias Soeken, Winston Jason Haaswijk, Benoît Laurent Auguste Seguin, Edo Collins

The slowing down of Moore's law and the emergence of new technologies puts an increasing pressure on the field of EDA. There is a constant need to improve optimization algorithms. However, finding and implementing such algorithms is a difficult task, espec ...

IEEE2018

Thin-film-specific elastic effects in ferroelectric domain structures

Konstantin Shapovalov

An essential feature of ferroelectric thin films is the presence in them of domain structures. In order to efficiently implement ferroelectric films into potential new ferroelectrics-based devices, it is of high interest to understand the behaviour of doma ...

EPFL2016

Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points

Fabio Nobile, Giovanni Migliorati

We study the accuracy of the discrete least-squares approximation on a finite-dimensional space of a real-valued target function from noisy pointwise evaluations at independent random points distributed according to a given sampling probability measure. Th ...

2015

The role of fluctuations in ecological patterns and processes

Andrea Giometto

Fluctuations are ubiquitous in nature and are relevant for nearly every ecological process. The main sources of fluctuations in population abundances are demographic and environmental stochasticity, whose effect on local population dynamics, metapopulation ...

EPFL2015

Dynamic crack propagation in a heterogeneous ceramic microstructure, insights from a cohesive model

Seyedeh Mohadeseh Taheri Mousavi

Silicon nitride is a widely used industrial ceramic, specifically for dynamic loading applications because of its special mechanical and material properties. The investigation of the underlying mechanisms which lead to these properties is experimentally ch ...

EPFL2015

Band offsets of lattice-matched semiconductor heterojunctions through hybrid functionals and G(0)W(0)

Alfredo Pasquarello, Wei Chen, Karim Steiner

To assess the accuracy of hybrid functional and many-body GW methods, we study the band offsets for a set of lattice-matched semiconductor heterojunctions, including AlAs/GaAs(100), AlP/GaP(100), Si/GaP(110), Ge/GaAs(110), Ge/AlAs(110), Ge/ZnSe(110), and Z ...

2014

Energy system design under uncertainty

Matthias Dubuis

The current context leads energy system design to very demanding objectives, due to their variety. Indeed, despite an increasing energy demand, environment indicators are becoming always more important. So that for a given service, emission (and then assoc ...

EPFL2012