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Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces

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This framework contains the results submitted on the paper "Negative Order Sobolev Cubatures: Preconditioners of Partial Differential Equation Learning Tasks Circumventing Numerical Stiffness",
submitted to IOP Machine Learning: Science and Technology ArXiv preprint is available, https://doi.org/10.48550/arXiv.2301.04887

The code was developed by Phil-Alexander Hofmann : [email protected], under the supervision of:
  - Juan-Esteban Suarez : [email protected]
  - Dr. Michael Hecht : [email protected]
All members of the Center for Advanced Systems Understanding (CASUS)

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Learning Partial Differential Equations by Spectral Approximates of General Sobolev Spaces

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