FRG: Collaborative Research: Singularities in Incompressible Flows: Computer Assisted Proofs and Physics-Informed Neural Networks

Information

  • NSF Award
  • 2403764
Owner
  • Award Id
    2403764
  • Award Effective Date
    12/1/2023 - 5 months ago
  • Award Expiration Date
    6/30/2026 - 2 years from now
  • Award Amount
    $ 138,009.00
  • Award Instrument
    Standard Grant

FRG: Collaborative Research: Singularities in Incompressible Flows: Computer Assisted Proofs and Physics-Informed Neural Networks

Whether three-dimensional incompressible flows develop singularities in finite time and whether (weak) solutions of Navier-Stokes equations are unique, are two of the most important problems in mathematical fluid dynamics. Any progress towards resolving these problems would have significant implications for the entire field. This project integrates theoretical proofs, numerical analysis, and machine learning for understanding singularities in fluids. Our recent investigations demonstrate that intersection between mathematical proofs and deep learning offers an exciting avenue for understanding how singularity occurs in fluids. Together, the five PIs encompass strengths in several areas such as mathematical analysis, numerical simulation, or computer-assisted proofs. In addition, the project will foster collaborations and increased interactions between the researchers at several leading research universities in the US, utilizing tools developed in one field to advance another, and promote learning and training of students and postdoctoral researchers with a goal of broadening the participation of researchers from underrepresented groups in the mathematical sciences.<br/><br/>The PIs will focus on three specific projects: (1) non-uniqueness of the Leray-Hopf solutions of the Navier Stokes equations in 3 dimensions, (2) formation of singularities for solutions of the three-dimensional Euler equations, and (3) optimization of physics-informed neural networks (PINN). Students, postdoctoral fellows, and visitors will be actively involved in these collaborations. To promote these exchanges research workshops will be organized once a year at the PIs’ institutions. These meetings will have two main objectives: a training objective, involving lectures to disseminate current ideas and progress; and an annual meeting of the PIs to review the progress and plan future steps. The PIs will also organize a summer school at Princeton University, aimed at graduate students and advanced undergraduate students. The summer school will have a scientific component, including minicourses on the mathematics of fluids, and a mentorship component, including a round table discussion regarding careers in mathematics and a women in mathematics panel.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

  • Program Officer
    Pedro Embidpembid@nsf.gov7032924859
  • Min Amd Letter Date
    12/8/2023 - 5 months ago
  • Max Amd Letter Date
    12/8/2023 - 5 months ago
  • ARRA Amount

Institutions

  • Name
    New York University
  • City
    NEW YORK
  • State
    NY
  • Country
    United States
  • Address
    70 WASHINGTON SQ S
  • Postal Code
    100121019
  • Phone Number
    2129982121

Investigators

  • First Name
    Tristan
  • Last Name
    Buckmaster
  • Email Address
    buckmaster@math.princeton.edu
  • Start Date
    12/8/2023 12:00:00 AM

Program Element

  • Text
    APPLIED MATHEMATICS
  • Code
    1266
  • Text
    ANALYSIS PROGRAM
  • Code
    1281

Program Reference

  • Text
    Machine Learning Theory
  • Text
    FOCUSED RESEARCH GROUPS IN MATH SCIENCES
  • Code
    1616