The Commonwealth has provided some of the world’s greatest mathematicians from Srinivasa Ramanujan FRS to Alan Turing FRS. Their heirs have refined and developed these fields to encompass the growing discipline of complexity science.
Some of the Commonwealth’s most outstanding researchers in these fields will talk about their cutting edge research, covering areas including quantum computing, climate prediction, number theory, artificial intelligence, fluid dynamics and computer networking.
There is no more challenging problem in computational science than that of predicting weather and climate reliably. A key challenge arises from the complexity of the system associated with the fact that the underlying dynamical laws of evolution are partial differential equations whose myriad scales are coupled nonlinearly. Contemporary supercomputers do not allow global weather and climate to resolve individual cloud systems, and this results is substantial uncertainty in estimates of future climate. Next generation exascale computers will help, but such computers are projected to be very energy intensive and this itself creates a variety of problems. Here we discuss a new paradigm for weather and climate prediction which exploits the concept of low-energy inexact computing for evolving the small-scale features of the climate system, features whose details are inherently uncertain because of the chaotic nature of climate.
The talk introduces contemporary methods and applications of an area called Computational Intelligence (CI). Aspects of CI are part of the Artificial Intelligence area, the latter established by Alan Turing, but CI is mainly concerned with the development and the applications of methods inspired by the principles of information processing in Nature. These methods include: evolutionary computation, inspired by the natural evolution and the work by Darwin; logic systems and fuzzy logic in particular, the latter established by Zadeh; brain-inspired artificial neural networks (ANN); quantum inspired computation using quantum principles discovered by Rutherford, Einstein and others.
The talk presents in more detail the recent development in ANN, including their methods, such as spiking neural networks and deep learning, and their highly parallel neuromorphic platforms of multimillion processing units – artificial neurons. Numerous have been the applications of ANN in all areas of science, technology, engineering, medicine, environment, ecology and others, especially when dealing with big data, stream data, decision support, distributed information processing.
The talk also announces some latest results by the author and his group based on novel ANN methods: NeuCube – a framework for a dynamic modelling and understanding of large and various multimodal spatio-temproal brain data; personalised predictive systems for stroke; pilot research on earthquake prediction systems. Future directions for CI systems include the use of more principles from mathematics, information science, bioinformatics and neuroinformatics in their integration .
 N. Kasabov, (ed) (2014) The Springer Handbook of Bio-/Neuroinformatics.
Reciprocity laws linking Galois representations and automorphic forms have been
one of the central themes in algebraic number theory. Wiles' deduced the proof of Fermat's Last Theorem as a corollary of a reciprocity law that linked Galois representations arising from elliptic curves to modular forms. In the lecture I will give a broad survey of the area and the recent developments in it.
This talk is about fluid flows which are stratified, in that temperature and concentrations of various components vary from one place to another. Such stratification occurs in oceans, in the Earth’s outer core, in clouds, in the chemical and food industries and in the context of reentry form space. It will be described how even a small viscosity stratification can have a large effect on flow stability, thus speeding up or retarding the transition to turbulence. This is due to the appearance of viscosity stratification in the singular perturbation term in the linearized equation for the dynamics. Similarly a small density stratification can have a large effect in generating instability if placed close to a vortex. These instabilities cause an initially laminar flow to go into decaying turbulence. The consequences of such instabilities for cloud flows and ocean mixing will be discussed.
Quantum mechanics is the ultimate theory of nature and when we succeed at building devices that function in uniquely quantum fashion then we may exceed classical limits on efficiency. This is true for quantum sensors, actuators and computers. Today we come close to realizing this quantum gain for some practical devices. The key next steps are to learn which quantum processes provide these benefits and how to engineer them. I will describe a few applications and examples of quantum sensors, actuators and processors.
Lead image: Page from Ramanujan's notebook on his Master Theorem by Srinavasa RamanujanDownload calendar
Sir William Wakeham FREng International Secretary, Royal Academy of Engineering (UK)
Professor Vincent Olunloyo Department of Systems Engineering, University of Lagos (Nigeria)
Professor Tim Palmer FRS Royal Society Research Professor, University of Oxford (UK)
Professor Nikola Kasabov Director, Knowledge Engineering & Discovery Research Institute (KEDRI), Auckland University of Technology (New Zealand)
Professor Chandrashekhar Khare FRS Professor of Mathematics, University of California, Los Angeles (USA)
Professor Rama Govindarajan Tata Institute of Fundamental Research, Centre for Interdisciplinary Sciences (India)
Professor Andy Hopper FRS President of the Institution for Engineering and Technology (tbc)