The fellow has produced three seminal publications as detailed in the publications section.
1. Combust. Flame 246 (2022) 112425. Criteria to switch from tabulation to neural networks in computational combustion
In this seminal work, the fellow has addressed a long-standing issue in the use of machine-learning over the use
of tabulation methods for solving generalised regression problems. In this work, theoretical bounds were developed
which quantify the performance of neural-networks over tabulation methods. The simple analytical formulas
essentially dictate which of the two methods is best to solve a regression problem. It was shown for instance
that neural-networks offer significant time/memory advantages for relatively large-parameter problems whereas
for small-parameter problems tabulation methods are more suitable to use. This work was also presented at the
19th international conference of numerical combustion (ICNC2024).
2. Comp. Fluids 255 (2023) 105840. An optimisation framework for the development of explicit discrete forward and inverse filters.
In this work, robust and efficient direct-inversion discrete filters were developed along with a reconstruction library.
The new filters allow filtered signals to be reconstructed at ease with order-of-magnitude time savings in comparison to
classic reconstruction methods. The new filters were also used to develop generalised reconstruction-based modelling
frameworks, and were tested using high-order direct simulation data.
3. Journal Fluid Mech. 983 (2024) A47. Revisiting the modelling framework for the unresolved scalar variance.
In this work, a novel theoretical modelling framework based on the concept of reconstruction was developed.
This new modelling framework is generalised, makes no assumptions about the underlying flow field, and
can be used to develop simulation models of arbitrary accuracy .Specifically, a variety of low-order and high-order
models for the unresolved scalar variance were developed as well as new models based on reconstruction.
The new models outperformed all classic models in the literature as evidenced by thorough testing using
high-order direct numerical simulation data.
