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Jul 22, 2020 - Go
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numerical-methods
Here are 914 public repositories matching this topic...
The Go kernel for Jupyter notebooks and nteract.
go
golang
data-science
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kernel
jupyter
zeromq
jupyter-notebook
nteract
artificial-intelligence
numerical-methods
gophernotes
C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types, continuous memory storage, and no pointers are involved
cpp
statistical-analysis
data-analysis
trading-strategies
trading-algorithms
numerical-methods
dataframe
financial-data
heterogeneous-lists
kmeans-clustering
numerical-analysis
heterogeneous
financial-engineering
multidimensional-data
affinity-propagation
heterogeneous-data
multi-dimensional-array
financial-data-analysis
large-data
dataframe-performance
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Sep 11, 2020 - C++
Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface.
python
c-plus-plus
multi-platform
openmp
mpi
parallel-computing
fem
bsd-license
numerical-methods
multiphysics
dem
kratos
kratos-multiphysics
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Sep 14, 2020 - C++
18.337 - Parallel Computing and Scientific Machine Learning
performance-engineering
parallelism
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differential-equations
gpu-computing
numerical-methods
lecture-notes
neural-ode
scientific-machine-learning
neural-sde
stiff-equations
scientific-simulators
sciml
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Sep 13, 2020 - HTML
Python library for arbitrary-precision floating-point arithmetic
python
special-functions
arbitrary-precision
complex-numbers
plotting
multiprecision
ordinary-differential-equations
numerical-methods
floating-point
numerical-optimization
numerical-integration
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Sep 6, 2020 - Python
Optimize floating-point expressions for accuracy
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Sep 9, 2020 - Racket
Numerical Analysis Implementations in Various Languages
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Jun 16, 2020 - C#
A C++ header-only library of statistical distribution functions.
statistics
cpp
constexpr
probability
eigen
openmp
quantile
cpp11
stats
armadillo
cdf
numerical-methods
blaze
distributions
eigen3
armadillo-library
density-functions
quantile-functions
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Sep 5, 2020 - C++
PDE-Net: Learning PDEs from Data
machine-learning
partial-differential-equations
applied-mathematics
numerical-methods
data-assimilation
inverse-problems
system-identification
data-driven-model
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May 24, 2020
Topological Data Analysis in Python
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Feb 20, 2019 - Python
Python package for numerical derivatives and partial differential equations in any number of dimensions.
scientific-computing
derivative
partial-differential-equations
finite-difference
numerical-methods
finite-differences
pde
finite-difference-coefficients
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Sep 10, 2020 - Python
An interactive book about the Riemann problem for hyperbolic PDEs, using Jupyter notebooks. Work in progress.
python
book
jupyter-notebook
mathematics
partial-differential-equations
numerical-methods
hyperbolic-equations
riemann-solver
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Jul 9, 2020 - HTML
Scientific Computing with Pharo
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Aug 30, 2020 - Smalltalk
Quantitative Interview Preparation Guide, updated version here ==>
finance
machine-learning
statistics
calculus
optimization
topic
linear-algebra
probability
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interview-questions
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numerical-methods
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theorem
interview-preparation
machine-learning-concepts
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Mar 23, 2019 - Jupyter Notebook
开源Go语言数值算法库(An open numerical library purely based on Go programming language)
go
interpolation
matrix
mathematics
scientific-computing
fitting
integral
linear-equations
numerical-methods
numerical
ode-solver
pde-solver
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Dec 11, 2019 - Go
santiagobadia
commented
Aug 12, 2020
A point load can be mathematically represented as a distribution, e.g., a Dirac delta. It breaks the Gridap
flow, since one cannot use Gauss quadratures and numerical integration (what we usually do in FEM) to compute the integral of f*v in that case.
I don't want to consider hacks, e.g., touch the vector entry in a particular node in which you want to put the force (assuming the force is on
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Data science and Big Data with Python
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Dec 11, 2019 - Jupyter Notebook
Open
Add test coverage
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ocramz
commented
Mar 1, 2019
Fortran Object-Oriented Differential-equations Integration Environment, FOODIE
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Mar 25, 2020 - Fortran
Escrita colaborativa de recursos educacionais abertos sobre cálculo numérico.
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Aug 24, 2020 - TeX
Numerical computation in native Haskell
matrix
linear-algebra
sparse-systems
matrix-factorization
sparse-data
numerical-calculations
scientific-computing
linear-systems
eigenvectors
scientific-computations
sparse-linear-systems
numerical-methods
iterative-methods
matrix-transpose
sparse-vectors
eigenvalues
matrix-computations
matrix-product
lu-factorization
iterative-linear-solvers
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Aug 21, 2020 - Haskell
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks
deep-learning
ode
neural-networks
partial-differential-equations
differential-equations
numerical-methods
ode-solver
solving-pdes
pde-solver
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Aug 31, 2020 - Jupyter Notebook
Finite Element tools in Julia
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Aug 31, 2020 - Julia
A LAPACK implementation for Go [DEPRECATED]
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Nov 23, 2018 - Go
Animations of random double pendulums
python
twitter-bot
twitter
animation
numpy
matplotlib
differential-equations
numerical-methods
numerics
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Jun 27, 2018 - Python
Accurate sums and dot products for Python.
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Jul 3, 2020 - Python
Solving linear, nonlinear equations, ordinary differential equations, ... using numerical methods in fortran
fortran
ode
integral
ordinary-differential-equations
linear-equations
nonlinear-equations
numerical-methods
sde
stochastic-differential-equations
plot-fortran
non-uniform-random-variate
quadrature-integration
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Feb 10, 2017 - Fortran
High level linear algebra library for Dlang
big-data
high-performance
numpy
matlab
linear-algebra
dlang
octave
blas
quantitative-finance
numerical-methods
ndslice
native-code
hedgefund
symmetry-investments
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Sep 8, 2020 - D
Collected study materials in Numerical Optimization ANU@MATH3514(HPC)
numerical-methods
numerical-optimization
convex-optimization
electronic-books
boyd
convex-optimisation
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May 6, 2019
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Heston model has accurate density approximations for European option prices, which are of interest.
The module implementing this method should live under tf_quant_finance/volatility/heston_approximation.py. It should support both European option puts and calls approximations. Tests should be in heston_approximation_test.py in the same folder.