
Jaggedtimestep technique improving convergence order of Fernandez's Explicit RobinNeumann scheme for the coupling of incompressible fluid with thinwalled structure
Inspired by Rybak's multipletimestep technique, jaggedtimestep techn...
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HighOrder Multirate Explicit TimeStepping Schemes for the BaroclinicBarotropic Split Dynamics in Primitive Equations
In order to treat the multiple time scales of ocean dynamics in an effic...
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Universality of the Langevin diffusion as scaling limit of a family of MetropolisHastings processes I: fixed dimension
Given a target distribution μ on a general state space X and a proposal ...
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Large deviations for the empirical measure of the zigzag process
The zigzag process is a piecewise deterministic Markov process in posit...
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Universality of the Langevin diffusion as scaling limit of a family of MetropolisHastings processes
Given a target distribution μ on a general state space X and a proposal ...
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Hypercoercivity properties of adaptive Langevin dynamics
Adaptive Langevin dynamics is a method for sampling the BoltzmannGibbs ...
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Axonal Conduction Velocity Impacts Neuronal Network Oscillations
Increasing experimental evidence suggests that axonal action potential c...
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Exact targeting of Gibbs distributions using velocityjump processes
This work introduces and studies a new family of velocity jump Markov processes directly amenable to exact simulation with the following two properties: i) trajectories converge in law when a timestep parameter vanishes towards a given Langevin or Hamiltonian dynamics; ii) the stationary distribution of the process is always exactly given by the product of a Gaussian (for velocities) by any target logdensity whose gradient is pointwise computabe together with some additional explicit appropriate upper bound. The process does not exhibit any velocity reflections (jump sizes can be controlled) and is suitable for the 'factorization method'. We provide a rigorous mathematical proof of: i) the small timestep convergence towards Hamiltonian/Langevin dynamics, as well as ii) the exponentially fast convergence towards the target distribution when suitable noise on velocity is present. Numerical implementation is detailed and illustrated.
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