MOOC

Fonctions Trigonométriques, Logarithmiques et Exponentielles

Publications associées (901)

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Localization errors of the stochastic heat equation

David Jean-Michel Candil

In this thesis, we study the stochastic heat equation (SHE) on bounded domains and on the whole Euclidean space

\R^d.

We confirm the intuition that as the bounded domain increases to the whole space, both solutions become arbitrarily close to one another ...

EPFL2022

Fonctions Trigonométriques, Logarithmiques et Exponentielles

Graph Chatbot

Chattez avec Graph Search

Propagation of singularities for the stochastic wave equation

Localization errors of the stochastic heat equation

Distributed Graph Learning With Smooth Data Priors

Unambiguous DNFs and Alon-Saks-Seymour

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

Generalised Implicit Neural Representations

On the robustness of the metric dimension of grid graphs to adding a single edge

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Distributed Graph Learning With Smooth Data Priors

Propagation of singularities for the stochastic wave equation

Localization errors of the stochastic heat equation

Unambiguous DNFs and Alon-Saks-Seymour

Differential Entropy of the Conditional Expectation Under Additive Gaussian Noise

DIVIDE-AND-CONQUER METHODS FOR FUNCTIONS OF MATRICES WITH BANDED OR HIERARCHICAL LOW-RANK STRUCTURE\ast

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks

Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions

On the robustness of the metric dimension of grid graphs to adding a single edge

Generalised Implicit Neural Representations