Stefano Wahono

Thesis Title

Data-Driven Reduced Order Modelling using Adaptively Sampled Reduced Basis Model

Research overview

Physics-based simulations are commonly used to study complex phenomena.  However, these computations are often too time-consuming, particularly for dynamical systems with a large number of input parameters.  This study develops a fully non-intrusive reduced order modelling (ROM) framework to construct a low-dimensional surrogate model of a general high-dimensional dynamical system.  The ROM construction seeks to use a data-driven machine learning (ML) approach to systematically represent full order system dynamics on a reduced basis space.  The ROM accuracy is improved using a closed loop adaptive sampling methodology which seeks to minimise both the ROM global error norms and statistical uncertainties.

Supervisors

Prof Richard Sandberg

Prof Chris Manzie

Qualifications

B.Eng. Mechanical Engineering, Monash University, Australia (2005)

B.Tech. Aerospace Engineering, Monash University, Australia (2005)