About the project
Research on epidemic dynamics and particle physics may seem worlds apart, but a common challenge is limiting progress in both fields: the need to explore computationally heavy Monte Carlo simulations in high detail across many dimensional parameter spaces. And in both cases, standard machine learning
approaches based on pre-trained models are of limited use. Recent work in particle physics at the University of Oslo has allowed the development of new tools that can address these problems, however so far its application to other fields has been limited. In this project, we will tackle this by developing a discipline-agnostic lightweight tool for continual learning, and use this to perform comprehensive, simulation-based explorations of epidemic dynamics and new particle physics theories.
This research aims to meet the United Nations Sustainable Development Goal 3, in particular Target 3.d: "Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks.". Our project will enable large and data-driven exploration of agent-based simulations of epidemics. This will allow a detailed investigation of the efficacy, combined effects and interaction dynamics of various government interventions, knowledge paramount in preparation for possible future outbreaks.
On the physics side, our project will allow us to probe deeper into the space of possible new physics models. In doing so, we directly extend the scientific impact of expensive, publicly funded experiments such as the LHC, and may pave the way for spectacular discoveries in future searches.
Financing
This project is financed as a Sustainability Project by the Faculty of Mathematics and Natural Sciences, University of Oslo.