Gaussian Process Regression for Data-Driven Model Predictive Control

Systems and Control deals with modelling and control design of many different types of systems with different behaviour and characteristics. Often times it is challenging to build mathematical models to describe the system dynamics due to limited knowledge of the underlying system, complex nonlinearities, or the existence of unknown exogenous...

Gaussian Process Models for Preliminary Low-Thrust Trajectory Optimization

Low-thrust trajectories can benefit the search for propellant-optimal trajectories, but increases in modeling complexity and computational load remain a challenge for efficient mission design and optimization. An approach for developing models utilizing Gaussian Process (GP) regression and classification is proposed to perform computationally...

Data - driven design of a metamaterial unit cell using sparse Gaussian processes

Metamaterials are a new class of materials where the properties crucially depend on the design of the unit cell that is periodically repeated in space. In this study a new metamaterial unit cell concept has been proposed, inspired by a class of space structures known as deployable masts. The ability of these structures to contract to a fraction...

Field inversion and machine learning in turbulence modeling

Turbulence closure models will continue to be necessary in order to perform computationally affordable simulations in the foreseeable future. It is expected that Reynolds-averaged Navier-Stokes (RANS) turbulence models will still be useful with the further development of the more accurate, but computationally expensive large eddy simulation (LES...