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Scott Field

faculty

Scott Field, PhD

Associate Professor

Mathematics

Curriculum Vitae

Contact

508-999-8281

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Spruce Hall 0174

Education

2011Brown UniversityPhD
2006University of RochesterBS

Teaching

Programs

Teaching

Courses

Scientific machine learning algorithms for computational science and engineering. Topics may include physics-informed neural networks, neural dynamical systems, AI-based surrogate models, signal detection with convolutional neural networks, learning nonlinear continuous operators, neural turbulence models, optimization algorithms, simulation-based Bayesian inference, and more. Python will be the primary language. Emphasis on real-world applications, covering high-performance computing with multi-core and GPU acceleration.

Supervised research on an experimental or theoretical topic in physics under a faculty advisor. This course is offered only to students indicating strong intention and ability to do thesis work in subsequent semesters. The credits are considered equivalent to Thesis (PHY 690) if thesis work on the same topic is taken up later. Otherwise, a written report is required at the end of the research. Graded A-F, or IP if the work is approved to be continued as PHY 690 Thesis, in which case the grade earned when the thesis is completed will replace the IP.

Research

Research awards

  • $ 189,022 awarded by NATIONAL SCIENCE FOUNDATION for Collaborative Research: CDS&E: Data-Driven Discovery of Neural ODE Dynamics, Astrophysical Models, and Orbits (Neural ODE DynAMO)
  • $ 349,101 awarded by National Science Foundation for Developing High Order Stable and Efficient Methods for Long Time Simulations of Gravitational Waveforms
  • $ 13,000 awarded by Mathematical Association of America for Mixed Model Implicit and IMEX Runge-Kutta Methods
  • $ 438,284 awarded by Office of Naval Research for UMassD MUST IV: Learning Nonlinear Dynamical Systems from Sparse and Noisy Data: Applications to Signal Detection and Recovery
  • $ 650,000 awarded by National Science Foundation for Implementation of a Contextualized Computing Pedagogy in STEM Core Courses and Its Impact on Undergraduate Student Academic Success, Retention, and Graduation

Research

Research interests

  • Gravitational wave data science
  • Discontinuous Galerkin methods
  • Large-scale Scientific Computation
  • Computational general relativity and fluid dynamics
  • Numerical analysis