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feat: Adaptive multilinear interpolation for OBL solver #3391
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…earInterpolatorStaticKernels
…nel to PythonFunction
…to PythonFunction
…g adaptive kernel
Thanks @av-novikov for this PR! I think that the python scripts shouldn't go in a subfolder of |
Thank you @sframba ! |
I think we should put all of these scripts in the python tools repository. |
@CusiniM @sframba Should I place interfaces in different folders in geosPythonPackages repo according to their purposes, e.g. input -> geos-xml-tools, mesh -> geos-mesh ? Or should i place everything to the same folder, e.g. makutu-utilities? |
I think we should keep them in the same package/folder in geosPythonPackages repo. That way we just have to do one pip install to get the python package up and running. Two ways we could go for:
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Dear @Bubusch @sframba @CusiniM |
This PR adds interfaces for embedding of Python function into C++ code, adds multilinear adaptive interpolation with given exact evaluator from Python and adds the support of both static and adaptive interpolation in ReactiveCompositionalMultiphaseOBLKernels. Ultimate goal is to run simulation similar to open-darts from a corresponding (part of) model given from Python.
FAQ:
Q: Why do we use __uint128_t ?
A: We need to count nodes and hypercubes in multidimensional state space. Even with 300 points per axis in 8-dim space (e.g. 8-component fluid), the number of points (300^8) surpasses the maximum of 64-bit integer. Moreover, single __uint128_t key type simplifies hashing points and hypercubes in std::unordered_map storage.
Q: Do we duplicate the storage of points?
A: Yes, because we minimize memory accesses by excessive, but consecutive storage.