8 papers
Collective Annealing by Switching Temperatures: a Boltzmann-type description
Frédéric Blondeel, Lorenzo Pareschi, Giovanni Samaey
The design of effective cooling strategies is a crucial component in simulated annealing algorithms based on the Metropolis method. Traditionally, this is achieved through inverse…
Two-Time-Scale Learning Dynamics: A Population View of Neural Network Training
Giacomo Borghi, Hyesung Im, Lorenzo Pareschi
Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast within-model optimisation wi…
High-Order Asymptotic-Preserving IMEX schemes for an ES-BGK model for Gas Mixtures
Domenico Caparello, Lorenzo Pareschi, Thomas Rey
In this work we construct a high-order Asymptotic-Preserving (AP) Implicit-Explicit (IMEX) scheme for the ES-BGK model for gas mixtures introduced in [Brull, Commun. Math. Sci., 20…
Swarm-based optimization with jumps: a kinetic BGK framework and convergence analysis
Giacomo Borghi, Hyesung Im, Lorenzo Pareschi
Metaheuristic algorithms are powerful tools for global optimization, particularly for non-convex and non-differentiable problems where exact methods are often impractical. Particle…
Hierarchical dynamic domain decomposition for the multiscale Boltzmann equation
Domenico Caparello, Lorenzo Pareschi, Thomas Rey
In this work, we present a hierarchical domain decomposition method for the multi-scale Boltzmann equation based on moment realizability matrices, a concept introduced by Levermore…
Wasserstein convergence rates for stochastic particle approximation of Boltzmann models
Giacomo Borghi, Lorenzo Pareschi
We establish quantitative convergence rates for stochastic particle approximation based on Nanbu-type Monte Carlo schemes applied to a broad class of collisional kinetic models. Us…