With the rapid development of new energy electric vehicles and smart grids, the demand for
batteries is increasing. The battery management system (BMS) plays a crucial role in the …

In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

This paper tackles the modelling of large, complex and multivariate time series panels in a
probabilistic setting. To this extent, we present a novel approach reconciling classical state …

The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-
linear dimensionality reduction that has been widely applied. However, the current approach …

State-space models have been successfully used for more than fifty years in different areas
of science and engineering. We present a procedure for efficient variational Bayesian …

State-space models (SSMs) are a highly expressive model class for learning patterns in time
series data and for system identification. Deterministic versions of SSMs (eg, LSTMs) proved …

Intention inference can be an essential step toward efficient human–robot interaction. For
this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically …

The Gaussian process state space model (GPSSM) is a non-linear dynamical system, where
unknown transition and/or measurement mappings are described by GPs. Most research in …

State-space models are successfully used in many areas of science, engineering and
economics to model time series and dynamical systems. We present a fully Bayesian …

In this paper, we revisit batch state estimation through the lens of Gaussian process (GP)
regression. We consider continuous-discrete estimation problems wherein a trajectory is …