Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

Resolved Stars' Insights into Galaxy Physics

Thesis (Ph.D.)--University of Washington, 2016-08

The Usability of a Commercial Game Physics Engine to Develop Physics Educational Materials: an Investigation

Commercial computer games contain “physics engine” components, responsible for providing realistic interactions among game objects. The question naturally arises of whether these engines can be used to develop educational materials for high school and university physics education. To answer this question, the author's group recently conducted a detailed scientific investigation of the physics engine of Unreal Tournament 2004 (UT2004). This article presents their motivation, methodology, and results. The author presents the findings of experiments that probed the accessibility and fidelity of UT2004's physics engine, examples of educational materials developed, and an evaluation of their use in high school classes. The associated pedagogical implications of this approach are discussed, and the author suggests guidelines for educators on how to deploy the approach. Key resources are presented on an associated Web site.

Learning Physics with the Unreal Tournament Engine

Computer games such as Unreal Tournament (UT2004 and UT3) contain a 'physics engine' responsible for producing believable dynamic interactions between players and objects in the three-dimensional (3D) virtual world of a game. Through a series of probing experiments we have evaluated the fidelity and internal consistency of the UT2004 physics engine. These experiments have then led to the production of resources which may be used by learners and teachers of secondary-school physics. We also suggest an approach to learning, where both teachers and pupils may produce learning materials using the Unreal Tournament editor 'UnrealEd'.

Multi-physics modelling of materials processes on high performance parallel computers

Abstract not available

Geometric Hamiltonian formulation of a variational problem depending on the covariant acceleration

We consider a second-order variational problem depending on the covariant acceleration, which is related to the notion of Riemannian cubic polynomials. This problem and the corresponding optimal control problem are described in the context of higher order tangent bundles using geometric tools. The main tool, a presymplectic variant of Pontryagin’s maximum principle, allows us to study the dynamics of the control problem.

A finite-volume computational mechanics framework for multi-physics coupled fluid-stress problems

Abstract not available

Prediction of defects in steel castings with a multi-physics numerical code

Abstract not available

The role of nuclear physics in supernovae and the evolution of neutron stars, Neutrino Opacities, Equation of State, Transport Coefficients, and Dark Matter Production

Thesis (Ph.D.)--University of Washington, 2016-08