Search Results for "finite difference"
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Showing 38 open source projects for "finite difference"
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MethodOfLines.jl
Automatic Finite Difference PDE solving with Julia SciML
MethodOfLines.jl is a Julia package for automated finite difference discretization of symbolically defined PDEs in N dimensions. It uses symbolic expressions for systems of partial differential equations as defined with ModelingToolkit.jl, and Interval from DomainSets.jl to define the space(time) over which the simulation runs. This project is under active development, therefore the interface is subject to change. -
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FiniteDifferences.jl
High accuracy derivatives, estimated via numerical finite differences
FiniteDifferences.jl estimates derivatives with finite differences. See also the Python package FDM. FiniteDiff.jl and FiniteDifferences.jl are similar libraries: both calculate approximate derivatives numerically. You should definitely use one or the other, rather than the legacy Calculus.jl finite differencing, or reimplementing it yourself. At some point in the future, they might merge, or one might depend on the other. -
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elastic-package
Package for finite difference solution of elastic wave equations
This package uses the summation-by-parts finite difference framework to facilitate numerical solution of elastic wave equations. -
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JUDI.jl
Julia Devito inversion
...The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling operators can also be used as layers in (convolutional) neural networks to implement physics-augmented deep learning algorithms thanks to its implementation of ChainRules's rrule for the linear operators representing the discre wave equation. -
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StructuralEquationModels.jl
A fast and flexible Structural Equation Modelling Framework
...We provide fast objective functions, gradients, and for some cases hessians as well as approximations thereof. As a user, you can easily define custom loss functions. For those, you can decide to provide analytical gradients or use finite difference approximation / automatic differentiation. You can choose to mix loss functions natively found in this package and those you provide. In such cases, you optimize over a sum of different objectives (e.g. ML + Ridge). This strategy also applies to gradients, where you may supply analytic gradients or opt for automatic differentiation or mixed analytical and automatic differentiation. ... -
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Library for ion optics, plasma extraction and space charge dominated ion beam transport.
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Book-Reviews-In-Mathematica
Partial Differential Equations, Complex Analysis, Mathematica, Farlow
Cliff* Notes, Mathematica Evaluatable "Partial Differential Equations for Scientists and Engineers", Farlow "Physics for Scientists and Engineers", Serway "A First Course in Complex Analysis", Beck "PDE", Asmar "PDE, An Introduction", Colton "Elementary Differential Equations", 7th, Rainville "Ordinary Differential Equations", Tenenbaum "Linear Algebra And It's Applications", Lay "Swokowski Calculus", 5th, Swokowski "Chemistry Concepts & Problems, A Self-Teaching Guide",... -
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elastic-curvilinear
SBP-SAT finite difference code for anisotropic elasticity
SBP-SAT finite difference code for general anisotropic elasticity in complex geometries -
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DiffEqOperators.jl
Linear operators for discretizations of differential equations
DiffEqOperators.jl is a package for finite difference discretization of partial differential equations. It allows building lazy operators for high order non-uniform finite differences in an arbitrary number of dimensions, including vector calculus operators. For the operators, both centered and upwind operators are provided, for domains of any dimension, arbitrarily spaced grids, and for any order of accuracy. -
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laplace-curvilinear
SBP-SAT finite difference code for the Laplacian in complex geometries
MATLAB code that generates all figures in [Almquist and Dunham, 2020, https://doi.org/10.1016/j.jcp.2020.109294 ]. The paper considers narrow-stencil summation-by-parts finite difference methods and derives new penalty terms for boundary and interface conditions. The new penalty terms are significantly less stiff than the previous state-of-the-art method on curvilinear grids. On highly skewed grids, there may be an order of magnitude or more difference in the time-step restriction. -
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acoustic-package
Package for finite difference solution of acoustic wave equations
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APBS
Biomolecular electrostatics software
This software has moved to http://www.poissonboltzmann.org/. -
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Finite Difference Nuclear Magnetic Resonance provides a set of tools for simulating NMR experiments in restricted geometries.
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GMES
GMES is a free Python package for FDTD electromagnetic simulations.
GMES is a free finite-difference time-domain (FDTD) simulation Python package developed at GIST to model photonic devices. Its features include simulation in 1D, 2D, and 3D Cartesian coordinates, distributed memory parallelism on any system supporting the MPI standard, portable to any Unix-like system, variuos dispersive ε(ω) models, CPML absorbing boundaries and/or Bloch-periodic boundary conditions, and arbitrary material and source distributions. -
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swe2d
Matlab 2D Shallow Water Solver
This is a set of matlab codes to solve the depth-averaged shallow water equations following the method of Casulli (1990) in which the free-surface is solved with the theta method and momentum advection is computed with the Eulerian-Lagrangian method (ELM). The free-surface equation is computed with the conjugate-gradient algorithm. Casulli, V. (1990) Semi-implicit finite difference methods for the two-dimensional shallow water equations, J. Comp. Phys., 86 (1), 56-74. -
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REINFORCEjs
Reinforcement Learning Agents in Javascript
REINFORCEjs is a JavaScript reinforcement-learning library built to demonstrate several major RL algorithms directly in the browser. It includes dynamic programming methods for finite environments and tabular temporal-difference learning with SARSA and Q-learning. A Deep Q-Learning agent extends Q-learning to continuous state features through neural-network function approximation. The project also experiments with stochastic and deterministic policy gradients and actor-critic methods for continuous action spaces. ... -
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FDAC3DMOD
an open source 3D acoustic forward simulation software package
fdac3dmod is an open source 3D acoustic forward simulation software. It solves 3D acoustic velocity-pressure equations via finite-difference time-domain (FDTD) method with perfectly matched layers (PML) used as the boundary condition. -
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KEMP
A FDTD solver for electromagnetic wave simulations on a GPU cluster
KEMP is a fast FDTD solver on a GPU-based cluster. The FDTD (Finite-Difference Time-Domain) method is a popular numerical method for electromagnetic field simulations. KEMP enables hardware accelerations suitable for multi-GPU, multi-core CPU and GPU cluster. KEMP also provide easy configuration by using Python scripting language. -
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rmhdtod
Code to solve RMHD equations for turbulence
This code is intended to solve the reduced magnetohydrodynamic equations. The code will be pseudospectral in the perpendicular dimensions and finite difference in the z direction. It is parallelised in two dimensions, y and z for real space variables and kx and z for fourier-space variables). -
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Acoustic FDTD Solver (AC2D) -- is a software to simulate acoustic wave propagation in two dimensions based on the finite-difference time-domain (FDTD) method.
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WOLFSIM: Wideband Optical FDTD Simulator
FDTD Electomagnetic Wave Simulation Software
WOLFSIM is a Finite-Difference Time-Domain electromagnetic simulator, designed to be easy to use but still very powerful, developed and maintained by researchers at North Carolina State University. It's features include: -1D, 2D, and 3D structures that are periodic in 1 or 2 dimensions -Materials that are anisotropic in permittivity and conductivity -Obliquely incident sources -Built-in vectorial (i.e. full polarization) near-to-far-field transformation See these publications for full details on the algorithm: http://www.ece.ncsu.edu/oleg/files-wiki/6/62/SPIE_12_Miskiewicz_wolfsim3D.pdf http://www.ece.ncsu.edu/oleg/files-wiki/1/13/OptExpress07_OH.pdf -
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CFD Free Surface 3D
Incompressible Navier–Stokes 3D water simulation with free surface
This is Incompressible Navier–Stokes 3D simple water simulation with free surface for Computational Fluid Dynamics.Uses Finite difference method and particles. -
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The parallelized FDTD Schrodinger Solver implements a parallel algorithm for solving the time-independent 3d Schrodinger equation using the finite difference time domain (FDTD) method. See the Hosted Apps > MediaWiki menu item for more information.
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The Finite Difference Time Domain (FDTD) method is a powerfull numerical technique to solve the Maxwell equations. Here you can find parallel FDTD codes developed by Zsolt Szabó. The codes can be run under UNIX and Windows operating systems.
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Visual Two Zone Model
Build up groundwater numerical modeling
...The conceptual model and computer native code was developed by Rushton and Redshaw (1979), Rathod and Rusthon (1984 and 1992), and the graphical user interface was developed by our Group Member, the hydrogeologist Noél Hernández Laloth. The model, based on finite-difference approximations using the “two-zone” conceptual approach, can provide a greater flexibility than analytical methods, because the aquifer system is approximated as two horizontal permeable zones with less permeable intermediate layers. When this approximation is adopted, it is possible to represent both radial and vertical components of flow (Rathod and Rusthon, 1992). ...
