Trillions of bytes of data have been created in the world while reading this very sentence.
The traditional triad of the scientific method, theory, experimentation and simulation has added big data now.
Central to big data science are artificial intelligence and machine learning, which are automated ways of transforming information into empirical knowledge.
Whereas empirical knowledge is crucial to many practical applications, such as facial recognition, empirical models do not necessarily fulfil physical principles, for example, conservation laws.
Physical principles provide physical equations, which are essential to understanding, modelling and, ultimately, designing engineering systems that optimize a performance.
One of the most prominent engineering and scientific communities that has been working with big data and physical principles for decades is fluid mechanics.
Fluid mechanics underpins many established and emerging world-wide industries as well as critical societal issues such as climate science and energy consumption.
These problems involve flows that are unsteady, unpredictable and uncertain across a vast range of spatiotemporal scales.
Because of this, numerical simulations and experimental measurements of the physics of fluids generate big data, which is to say that fluid mechanics is both a physical and big data discipline.
To gain insight into this big data, machine learning techniques have recently been applied to benchmark fluids problems with success, but there are still three big open questions:
(i) Do machine learning algorithms scale to engineering configurations? (Are they robust?);
(ii) Can we gain physical insight into the solutions? (Are they interpretable?); and
(iii) Can we extrapolate knowledge to other configurations, such as multi-physics phenomena? (Are they generalizable?).
Key to this project is the simple, but important, observation that the ability of fluid mechanics modelling to predict the evolution of a flow is enabled by both empirical approaches and physical principles.
Machine learning models may not be interpretable, robust and generalizable, but they enable adaptive empirical modelling.
On the other hand, physical principles are governed by “rigid equations”, i.e. they do not adaptively change, but they are interpretable, robust and generalizable.
Machine learning is excellent at finding correlations in big data, whereas human beings are excellent at extrapolating knowledge (physical principles).
This project will integrate the complementary capabilities of both approaches to achieve adaptive modelling for optimization of unsteady, unpredictable and uncertain multi-physics flows.
These are flows of paramount importance in engineering, which are intractable to modelling and optimization.
This project will combine physical principles and empirical modelling into a unified approach: physics-constrained adaptive learning.
The technical objectives are
(i) to combine physical principles with machine learning empirical modelling into a unified approach;
(ii) to design adaptive multi-physics models by on-the-fly assimilation of experimental measurements (direct problem);
(iii) to optimize unsteady, unpredictable and uncertain flows (inverse problem);
(iv) to accompany the predictions with robust uncertainty quantification;
and (v) to develop a code that wraps around existing high-fidelity simulation software and experimental rigs.
The computational technology will be applied to maximize energy harvesting from aeroelastic systems to produce clean energy;
optimize clean and stable aeroengines with lean flames for low emissions; and reconstruct high-resolution physics from low-resolution experimental measurements to maximize physical information to assimilate in our simulations.
The vision is that machine learning methods will go hand in hand with the art of constructing physical models.
This has the potential to revolutionize the engineering design of multi-physics fluid dynamics systems, and beyond.
