In this letter, we consider the problem of deriving and incorporating accurate dynamic
models for model predictive control (MPC) with an application to quadrotor control. MPC …

Digital Twins have much attention in the ship** industry, attempting to support all phases
of a vessel's life cycle. With several tools appearing in Digital Twin software suites, high …

Model Predictive Control (MPC) is a state-of-the-art (SOTA) control technique which requires
solving hard constrained optimization problems iteratively. For uncertain dynamics …

This study examined the integration of machine learning (ML) techniques into advanced
manufacturing processes to enhance predictive maintenance, quality control, and process …

Extracting predictive models from nonlinear systems is a central task in scientific machine
learning. One key problem is the reconciliation between modern data-driven approaches …

A new methodology for rigorous Bayesian learning of high-dimensional stochastic
dynamical models is developed. The methodology performs parallelized computation of …

Persistent monitoring of a spatiotemporal fluid process requires data sampling and
predictive modeling of the process being monitored. In this paper we present PASST …

In this paper, a Takenaka-Malmquist (TM) basis function based equivalent data-model is
established by an adaptive rational decomposition for the finite-time interval trajectory …

We develop a network model that combines several ribosome flow models with extended
objects (RFMEO) competing for the finite pool of ribosomes. This alleviates the need to …

Two elementary models of ocean circulation, the well-known double-gyre stream function
model and a single-layer quasi-geostrophic (QG) basin model, are used to generate flow …