Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for
Bayesian inference in state-space models. In their original derivation, APFs operate in an …

Motivated by the challenge of incorporating data into misspecified and multiscale dynamical
models, we study a McKean–Vlasov equation that contains the data stream as a common …

First we show that physics-informed neural networks are not suitable for a large class of
parabolic partial differential equations including the Fokker-Planck equation. Then we …

In this paper, we investigate the averaging principle for a stochastic coupled fast-slow
atmosphere-ocean model. Precisely, we will first prove regularity estimates and tightness of …

In this paper we devise a deep learning algorithm to find non-trivial zeros of Fokker-Planck
operators when the drift is non-solenoidal. We demonstrate the efficacy of our algorithm for …

In this paper, we study the averaging principle for a stochastic coupled fast–slow
atmosphere–ocean model with non-Lipschitz Lévy noise. Precisely, we will first explore the …

In this paper, we combine tools from the study of chaotic dynamical systems with nonlinear
non-Gaussian data assimilation algorithms to produce novel particle filtering algorithms …

This paper proposes a nudged particle filter for estimating the pose of a camera mounted on
flying robots collecting a video sequence. The nudged particle filter leverages two image-to …

Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for
Bayesian inference in state-space models. In their original derivation, APFs operate in an …

This paper considers the approximation of the continuous time filtering equation for the case
of a multiple timescale signal (slow-intermediate and fast scales) that may have correlation …