F. Liu

Y. Zhang

O. Dahlsten

F. Wang

Machine learning techniques enhance the efficiency of energy harvesters by implementing reversible energy-conserving operations.

We probe the potential for intelligent intervention to enhance the power output of energy harvesters. We investigate general principles and a case study: a bi-resonant piezo electric harvester. We consider intelligent interventions via pre-programmed reversible energy-conserving operations. these include voltage bias  ips and voltage phase shifts. These can be used to rectify voltages and to remove destructive interference. We choose the intervention type based on past data, using machine learning techniques. We  nd that in important parameter regimes the resulting interventions can outperform diode-based intervention, which in contrast has a fundamental minimum power dissipation bound.

Intelligently chosen interventions have potential to outperform the diode bridge in power conditioning

Energy harvesting with AI

Next generation structures and materials are key to aerospace, the built environment and exploring space. Hierarchical materials, fractal structures, and polydisperse systems offer dramatic gains in efficiency. Advances in 3D printing and self-assembly mean that these novel technologies can be practically manufactured.

Extreme materials

Silicon-based electronic computing is hitting energetic and technological barriers, and quantum computing remains a theoretical challenge of the future. To maintain an exponential increase in computing power, we require alternative frameworks. Two which we are studying are memristors and photonic computing.

Low-energy computing

Information science underpins modern society. Quantum information science is creating the next generation of information technology, where effects like superposition and entanglement are exploited to envisage qualitatively new technologies. This field also gives an alternative perspective on physics, wherein information takes centre stage.

Quantum information

Innovation is to organizations what evolution is to organisms: it is how organisations adapt to changes in the environment and improve. New mathematical models of component recombination and the building blocks of economic complexity offer fundamental insights into how firms, countries and technologies develop.

Science of innovation

The global behaviour of local processes depends on the geometry of their underlying space. Studying processes on ideal graphs and on irregular networks derived from real data uncovers surprising behaviour in biological, social and financial systems. The tools of this field include spectral, replica, community and combinatoric techniques.

Graph theory

Institutional risk originates from ordinary economic uncertainties. But when these risks propagate across inter-connected institutions, internal amplification can lead to dramatic exposures. These systemic risks can be mitigated by applying ideas from cascading failures, diversification and extreme value theory.