NSGP - Non-Stationairy Gaussian Process Regression
A Python implementation of Non stationary Gaussian Process Regression 
Posts by Collection
portfolio
Reinforcement Learning on bluf poker
Teaching an agent to play dice. Reinforcment learning for a simple dice game. 
Constraint Optimisation - Team assignment
Optimizing team assignment. Constraint optimisation tool to find the optimal solution in a many-constraint system. 
Genetic Neural Networks for Coarse Scale FEM Correction
Genetic Neural Networks applied to 1D Advection-Diffusion physics for course scale correction. 
Predictive Maintenance in Membrane pumps
BSc Thesis Predictive Maintenance - Mimimising downtime by predicting equipment failure before it happens. 
MyRIO Biometric Bodysuit
Internship at VI Technologies to build a LabVIEW based biometric bodysuit. 
publications
Reduced Order Models for Non-Newtonian Fluids Using Non-Stationary Gaussian Process Regression
Published in , 2025
This (non-published) paper was written for team internship and explores how adaptive learning can be used to reduce the need for data in reduced order moddeling approaches for FEM simulations. The result is a python package for non-stationairy Gaussian Process Regression (NSGPR) and a method which needs a third of the data to gain the same accuracy.
talks
Talk 1 on Relevant Topic in Your Field
Published:
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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