SIMSMART’s main research topics are the following:
– Particle methods and rare event simulation.
– Advanced particle filtering and data assimilation (non-parametric inference, high dimension).
– Model reduction and sparsity.
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 and the increasing complexity of models also suggest
to devote more 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 and compare complex and observed dynamical systems depending on uncertain parameters motivates the construction of relevant reduced-order models.
One of the main classes of algorithms that encompass the three latter topics consist of ‘particle Monte Carlo methods’. In such methods,
several copies – a.k.a. particles – of the random system at stake are simulated, while being
split or killed according to some importance rules, for instance based on some real observations and its
associated likelihood (particle filtering), or on some score function (rare event simulation).