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1
Intro
2
Contents
3
A current-density formulation of the heat equation
4
Variational characterization
5
Heat equation in probability space
6
Comments
7
Linear Boltzmann equations
8
Probabilistic interpretation
9
Boltzmann-Grad limit of the Lorentz gas
10
Rayleigh-Boltzmann equation
11
Linear phonon Boltzmann equation
12
A current-measure formulation of LBE
13
Assumptions
14
Existence ad uniqueness
15
Definition of LBE
16
Remarks
17
Empirical measure and current of N independent copies
18
Continuity equation
19
Large deviation principle
20
Microscopic model
21
The collision kernel
22
Large deviations (work in progress)
23
The rate functional
24
Entropy dissipation inequality
25
The Dirichlet integral and the the kinematic term
26
A gradient flow formulation
Description:
Explore a gradient flow approach to kinetic equations in this 58-minute lecture from the Hausdorff Trimester Program on Kinetic Theory. Delve into the research conducted by Giada Basile, D. Benedetto, and L. Bertini, focusing on a gradient flow formulation of linear kinetic equations using an entropy dissipation inequality. Examine the relationship between this formulation and the large deviation principle for continuous time Markov chains, and consider potential extensions to non-linear cases. Learn about the current-density formulation of the heat equation, linear Boltzmann equations, and their probabilistic interpretations. Investigate the Boltzmann-Grad limit of the Lorentz gas, Rayleigh-Boltzmann equation, and linear phonon Boltzmann equation. Explore the current-measure formulation of LBE, including assumptions, existence, and uniqueness. Dive into topics such as empirical measure and current of N independent copies, continuity equation, large deviation principle, and microscopic models. Conclude with an examination of the rate functional, entropy dissipation inequality, Dirichlet integral, and kinematic term, culminating in a comprehensive gradient flow formulation. Read more

A Gradient Flow Approach to Kinetic Equations

Hausdorff Center for Mathematics
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