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Geometric Integration Methods for PDEs: Exploiting Mathematical Links between Structures

This presentation by Chris Budd explores the use of geometric integration methods to capture the behavior of PDE solutions in higher dimensions. The talk covers topics such as variational calculus, Hamiltonian systems, and the application of discrete variational calculus. The speaker also discusses the challenges and issues associated with using these methods for singular problems and proposes a new approach that balances scales in d dimensions.

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Geometric Integration Methods for PDEs: Exploiting Mathematical Links between Structures

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Presentation Transcript

  1. Some Geometric integration methods for PDEs Chris Budd (Bath)

  2. Have a PDE with solution u(x,y,t) Variational structure Symmetries linking space and time Conservation laws Maximum principles

  3. Cannot usually preserve all of the structure and Have to make choices Not always clear what the choices should be BUT GI methods can exploit underlying mathematical links between different structures

  4. Variational Calculus Hamiltonian system

  5. NLS is integrable in one-dimension, In higher dimensions Can we capture this behaviour?

  6. Discrete Variational Calculus [B,Furihata,Ide]

  7. Example: • Implementation : • Predict solution at next time step using a standard implicit-explicit method • Correct using a Powell Hybrid solver

  8. Problem: Need to adaptively update the time step Balance the scales

  9. t n

  10. U n

  11. t

  12. u x

  13. Some issues with using this approach for singular problems • Doesn’t naturally generalise to higher dimensions • Doesn’t exploit scalings and natural (small) length scales • Conservation is not always vital in singular problems Peak may not contribute asymptotically NLS

  14. Extend the idea of balancing the scales in d dimensions Need to adapt the spatial variable

  15. Use r-refinement to update the spatial mesh Generate a mesh by mapping a uniform mesh from a computational domain into a physicaldomain F Use a strategy for computing the mesh mapping function F which is simple, fast and takes geometric properties into account [cf. Image registration]

  16. Introduce a mesh potential Geometric scaling Control scaling via a measure

  17. Evolve mesh by solving a MK based PDE (PMA) Spatial smoothing (Invert operator using a spectral method) Ensures right-hand-side scales like P in dD to give global existence Averaged measure Parabolic Monge-Ampere equation PMA

  18. Geometry of the method Because PMA is based on a geometric approach, it performs well under certain geometric transformations 1. System is invariant under translations and rotations 2. For appropriate choices of M the system is invariant under natural scaling transformations of the form

  19. PMA is scale invariant provided that

  20. Extremely useful property when working with PDEs which have natural scaling laws Example: Parabolic blow-up in d-D Scale: Regularise:

  21. Solve in PMA parallel with the PDE 10 10^5 Solution: Y X Mesh:

  22. Solution in the computational domain 10^5

  23. NLS in 1-D

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