Simulation-based engineering has been a major protagonist of the technology of the last
century. However, models based on well established physics fail sometimes to describe the …

In this work, we consider the task of improving the accuracy of dynamic models for model
predictive control (MPC) in an online setting. Although prediction models can be learned …

Port Hamiltonian systems have grown in interest in recent years due to their modular
property, close relation with physical modelling and the interesting properties arising from …

Predicting high-dimensional short-term time-series is a difficult task due to the lack of
sufficient information and the curse of dimensionality. To overcome these problems, this …

Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable
both the elucidation of fundamental mechanisms and the engineering of matter for desired …

The explosive growth of civil applications of Unmanned Aerial Vehicles (UAVs) calls for
control algorithms that enable safe and trustworthy operations, especially in complex …

When a mobile robotic manipulator interacts with other robots, people, or the environment in
general, the end-effector forces need to be measured to assess if a task has been completed …

Implicit stochastic models, including both 'deep neural networks'(dNNs) and the more recent
unsupervised foundational models, cannot be explained. That is, it cannot be determined …

Featured Application Aiming at reducing the machining deformation of aero-engine casing,
this paper proposes a method to monitor the deformation of the part by use of the variation of …

Fine-grained change detection in sensor data is very challenging for artificial intelligence
though it is critically important in practice. It is the process of identifying differences in the …