Dr. Scott Field
Research experience
I completed my PhD under the supervision of Jan Hesthaven at Brown University.
My research interests lie broadly in:
- gravitational-wave modeling
- Bayesian inference for large datasets
- numerically solving partial differential equations that arise in the study of general relativity, and more recently:
- applications of machine learning to model discovery
Research projects
My research interests include Bayesian inference, surrogate and reduced order modeling, gravitational-wave data science, scientific and high performance computing, object and task-based parallel programing, scalable and parallel algorithms, discontinuous Galerkin methods, computational general relativity and fluid dynamics, and near-field to far-field methods.
The applications I have been most interested in are motivated primarily by general relativity, gravitational waves, and large datasets.
Our research group is a member of the Simulating eXtreme Spacetimes collaboration, the Center for Scientific Computing and Visualization Research, the UMass-URI Gravity Research Consortium, and the LISA consortium. Our work has been supported through National Science Foundation grants.
Potential research projects for students
Discovering dynamical system models through scientific machine learning techniques. In this data-driven process, measurements of the system are made, and from this data an optimization problem can be solved to isolate the most likely physical model (differential equations) that would deliver these physical measurements. I’m interested in the application of this approach to learning mechanical models of binary black hole (BBH) systems, where the physical measurements are gravitational waves.
Accurate and efficient methods for the numerical simulation of gravitational waves. Gravitational waves were predicted by Einstein himself a century ago and were recently observed for the first time in 2015. Ongoing observations of these waves from compact binary systems will be used to obtain additional information about exotic astrophysical objects in the universe like black holes and neutron stars. This project aids in the development of new computational techniques to meet the high-accuracy and high-efficiency requirements set by the LIGO and LISA data-analysis effort. Specifically, we are exploring discontinuous Galerkin and WENO methods to compute gravitational waves from large- to extreme-mass ratio black hole systems.
Data-driven gravitational-wave models. This project will explore a multi-disciplinary approach to gravitational-wave modeling to produce new algorithms and computer programs aimed at maximizing the scientific output of gravitational wave observations. The models produced as part of this research will be especially useful for analyzing black hole mergers and novel eccentric binary black hole systems. In particular, I am interested in combining traditional regression techniques (e.g. polynomials), reduced-order modeling, and machine learning to produce accurate waveform models using high-fidelity numerical simulations.
Contact
email: sfield+at+umassd+dot+edu
phone: 508-999-8318
