Presentation
Towards Neural Green's Operators for Magnetic Fusion
Presenter
DescriptionOperator networks have emerged as promising machine learning tools for reduced order modeling of a wide range of physical systems described by partial differential equations (PDEs). This work describes a new architecture for operator networks that approximates the Green's operator to a linear PDE. Such a ‘Neural Green’s Operator’ (NGO) acts as a surrogate for the PDE solution operator: it maps the PDE’s input functions (e.g. forcings, boundary conditions, material parameters) to its solution. We apply NGOs to relevant canonical PDEs and ask the question whether the NGO architecture would lead to significant computational benefits and conclude the discussion with numerical examples.
TimeTuesday, June 411:00 - 11:30 CEST
LocationHG F 26.3
Session Chairs
Event Type
Minisymposium
Physics
Computational Methods and Applied Mathematics