Context
SIMSMART is a computational probability and statistics research team, dedicated to the study of Monte Carlo and model reduction algorithms within a mathematical perspective.
The main applications of interest are related to the simulation and statistical inference of stochastic complex dynamical physical systems; in particular systems arising in meteorology and computational physics.
Using an appropriate level of mathematical abstraction and generalization, SIMSMART aims at providing non-superficial answers to methodological challenges related computational complexity reduction, statistical variance reduction, and uncertainty quantification.
Those challenges arise in a context where computational power is nurturing scientists into simulating the most detailed features of physical reality, relying on computationally demanding and high dimensional models and data sets.
Research Topics
SIMSMART’s main research topics are the following:
– Model reduction and sparsity.
– Sequential-like Monte Carlo, rare event simulation, Markov processes.
– Advanced particle filtering and data assimilation (non-parametric inference, high dimension).
Rare event simulation is ubiquitous in simulation, either to accelerate the occurrence of physically relevant slow or rare phenomena or to estimate the risk associated with uncertain variables.
The increasing size of recorded observational data also suggest
to devote effort into advanced filtering/data assimilation Monte Carlo methods, where managing high dimension, non-linearities and non-parametric models are current open challenges.
The need to simulate complex and high dimensional systems also motivates the construction of relevant reduced-order models and sparse representations.
