DIMON: Learning Solution Operators of Partial Differential Equations on a Diffeomorphic Family of Domains

The solution of a PDE over varying initial/boundary conditions on multiple domains is needed in a wide variety of applications, but it is computationally expensive if the solution is computed de novo whenever the initial/boundary conditions of the domain change. We introduce a general operator learning framework, called DIffeomorphic Mapping Operator learNing (DIMON) to learn approximate PDE solutions over a family of domains \Ωθ_θ, that learns the map from initial/boundary conditions and domain Ω_θ to the solution of the PDE, or to specified functionals thereof. DIMON is based on transporting a given problem (initial/boundary conditions and domain Ωθ) to a problem on a reference domain Ω₀, where training data from multiple problems is used to learn the map to the solution on Ω₀, which is then re-mapped to the original domain Ωθ. We consider several problems to demonstrate the performance of the framework in learning both static and time-dependent PDEs on non-rigid geometries; these include solving the Laplace equation, reaction-diffusion equations, and a multiscale PDE that characterizes the electrical propagation on the left ventricle. This work paves the way toward the fast prediction of PDE solutions on a family of domains and the application of neural operators in engineering and precision medicine.