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Software . 2019
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Software . 2019
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scikit-finite-diff, a new tool for PDE solving

Authors: Nicolas Cellier; Christian Ruyer-Quil;

scikit-finite-diff, a new tool for PDE solving

Abstract

Scikit-FDiff is a new tool, written in pure Python, that focus on reducing the time between the developpement of the mathematical model and the numerical solving. It allows an easy and automatic finite difference discretization, thanks to a symbolic processing that can deal with systems of multi-dimensional partial differential equation with complex boundary conditions. Using finite differences and the method of lines, it allows the transformation of the original PDE into an ODE, provinding a fast computation of the temporal evolution vector and the Jacobian matrix. The later is pre-computed in a symbolic way and sparsed by nature. It can be evaluated with as few computational ressources as possible, allowing the use of implicit and explicit solvers at a reasoneable cost.

{"references": ["LeVeque, R J (2002). Finite Volume Methods for Hyperbolic Problems.", "Cellier, N (2018). Optimisation d'\u00e9changeurs \u00e0 films ruisselants.", "Meurer, Aaron and al. (2017). SymPy: symbolic computing in Python.", "Rang, Joachim (2015). Improved traditional Rosenbrock-Wanner methods for stiff ODEs and DAEs."]}

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Keywords

python, finite difference, pde, physical modelling

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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