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Finite Difference Python Packages

Python packages with the GitHub topic finite-difference. Sorted by relevance, with stars and monthly downloads.
maroba
findiff

Python package for numerical derivatives and partial differential equations in any number of dimensions.

78K 507 70
simpeg
discretize

Discretization tools for finite volume and inverse problems.

27K 199 37
devitocodes
devito

DSL and compiler framework for automated finite-differences and stencil computation

12K 706 257
wesselb
fdm

Estimate derivatives with finite differences

10K 17 3
stfc
psyclone

PSyclone is a source-to-source Fortran compiler designed to programmatically optimise, parallelise and instrument HPC applications via user-provided transformation scripts.

4K 134 34
fancompute
fdfdpy

Pure Python implementation of the finite difference frequency domain (FDFD) method for electromagnetics

1K 67 20
gpavanb1
splitfxm

1D Finite-Difference/Volume with AMR and steady-state solver using Newton and Split-Newton with sparse Jacobian

567 6 0
ipselium
nsfds2

Navier-stokes solver for acoustics

523 1 1
INTERA-Inc
pyvista-gridder

Mesh generation using PyVista

510 28 2
gpavanb1
splitfdm

1D Finite-Difference with AMR and steady-state solver using Newton and Split-Newton

232 2 0
olivertso
pdepy

A Finite-Difference PDE solver.

217 10 1
carlobortolan
quantrs

Python bindings for a tiny library for quantitative finance (powered by Rust)

148 22 5
stefanmeili
fastfd

A library for building finite difference simulations

142 36 6
larsgeb
psvwave

Forward code for the P-SV wave equation on a staggered grid, with full waveform inversion interfaces. Finite difference approach according to stress-velocity formulation.

135 85 11
vyastreb
reynoldsflow

Efficient finite-difference solver for the Reynolds equation in thin fluid films

115 7 0
draktr
findi-descent

FinDi: Finite Difference Gradient Descent can optimize any function, including the ones without analytic form, by employing finite difference numerical differentiation within a gradient descent algorithm.

115 0 0
ghbrown
taylor

Generic derivative objects (gradients, Jacobians, Hessians, and more) by finite differences

75 0 0
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