3 ms·
I’m perfectly familiar with SymPy and it’s great but it doesn’t have methods comparable in performance in stiff PDEs to CVODE, and it’s not parallelised either.
by physicsguy 1y ago
I’m perfectly familiar with SymPy and it’s great but it doesn’t have methods comparable in performance in stiff PDEs to CVODE, and it’s not parallelised either. CVODES offers sensitivity analysis, ARKODE offers multi rate integrators for systems where the ODE can be decomposed into slow and fast rates, etc. etc. - it’s a much more sophisticated and specialist library.
- westurner 1y agoCVODE,: https://github.com/ufz/cvode https://github.com/ufz/cvode scikits.odes supports CVODE: scikits.odes.sundials.cvode: https://bmcage.github.io/odes/master/api/compat.html#module-scikits.odes.sundials.cvode https://bmcage.github.io/odes/master/api/compat.html#module-.... sckits.odes docs > Choosing a Solver: https://scikits-odes.readthedocs.io/en/latest/solvers.html https://scikits-odes.readthedocs.io/en/latest/solvers.html scipy.integrate.solve_ivp has Radau, BDF, and LSODA for stiff ODEs, in Python: https://docs.scipy.org/doc/scipy/reference/generated/scipy.integrate.solve_ivp.html https://docs.scipy.org/doc/scipy/reference/generated/scipy.i... If you add Arrow RecordBatch or Table output to CVODE with arrow-cpp, e.g. Dask can zero-copy buffers to Python (pyarrow, pandas.DataFrame(dtype_backend=arrow), or narwhals) when it needs to gather / fan in at a computational barrier in a process-parallel workflow. Is sklearn-deap useful with scikits.odes and sundials (and dask or not)?