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Abstract: Dedalus solves differential equations using spectral methods. It implements flexible algorithms to solve initial-value, boundary-value, and eigenvalue problems with broad ranges of custom equations and spectral domains. Its primary features include symbolic equation entry, multidimensional parallelization, implicit-explicit timestepping, and flexible analysis with HDF5. The code is written primarily in Python and features an easy-to-use interface. The numerical algorithm produces highly sparse systems for many equations which are efficiently solved using compiled libraries and MPI.

Credit: Burns, Keaton J.; Vasil, Geoffrey M.; Oishi, Jeffrey S.; Lecoanet, Daniel; Brown, Benjamin

Site: https://github.com/DedalusProject/dedalus
http://adsabs.harvard.edu/abs/2016ApJ...832...71L

Bibcode: 2016ascl.soft03015B

Preferred citation method: http://adsabs.harvard.edu/abs/2016ascl.soft03015B

ID: ascl:1603.015

- changed authors from Burns, Keaton; Brown, Ben; Lecoanet, Daniel; Oishi, Jeff; Vasil, Geoff per change request

- edited abstract per change request; previous was:
Dedalus solves differential equations using spectral methods. It is designed to solve initial-value, boundary-value, and eigenvalue problems involving nearly arbitrary equations sets and implements a highly flexible spectral framework that can simulate many domains and custom equations. Its primary features include symbolic equation entry, spectral domain discretization, multidimensional parallelization, implicit-explicit timestepping, and flexible analysis with HDF5. The code is written primarily in Python and features an easy-to-use interface, including text-based equation entry. The numerical algorithm produces highly sparse systems for a wide variety of equations on spectrally-discretized domains; these systems are efficiently solved by Dedalus using compiled libraries and multidimensional parallelization through MPI.