Hypocoercivity for a linearized multi-species Boltzmann system
Esther Sarah Daus, Ansgar J\"ungel, Cl\'ement Mouhot, and Nicola, Zamponi

TL;DR
This paper establishes a spectral gap estimate for the linearized multi-species Boltzmann operator, demonstrating exponential convergence to equilibrium using hypocoercivity methods, with explicit constants and spectral analysis.
Contribution
It introduces a new coercivity estimate for the spectral gap of the multi-species Boltzmann operator, including two proofs and explicit convergence rates.
Findings
Spectral gap estimate for multi-species Boltzmann operator
Explicit convergence rate to equilibrium on the torus
Calculation of essential spectra of the operators
Abstract
A new coercivity estimate on the spectral gap of the linearized Boltzmann collision operator for multiple species is proved. The assumptions on the collision kernels include hard and Maxwellian potentials under Grad's angular cut-off condition. Two proofs are given: a non-constructive one, based on the decomposition of the collision operator into a compact and a coercive part, and a constructive one, which exploits the "cross-effects" coming from collisions between different species and which yields explicit constants. Furthermore, the essential spectra of the linearized collision operator and the linearized Boltzmann operator are calculated. Based on the spectral-gap estimate, the exponential convergence towards global equilibrium with explicit rate is shown for solutions to the linearized multi-species Boltzmann system on the torus. The convergence is achieved by the interplay between…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGas Dynamics and Kinetic Theory · Mathematical Biology Tumor Growth · Advanced Numerical Methods in Computational Mathematics
