Software
Software development is an integral part of our research.
In order for new numerical algorithms to have impact, they must be implemented in software
that is both easy to use and powerful enough to solve interesting problems.
We believe in the principles of
reproducibility
and
open
science.
All codes used in our papers are released (under
modified BSD license) and we encourage others to use
them.
Below you will find brief descriptions of and links to the software packages developed
within our group and with collaborators. Smaller scripts used for a specific paper can be found
in links on our
publications page.
Documentation
Code
Paper
A parallel finite volume solver that includes the algorithms of
Clawpack and
SharpClaw. Scalable to tens of thousands of processors, with a convenient Python interface.
Documentation
Code
Paper
A collection of tools for designing
Runge-Kutta and multistep Runge-Kutta methods
with prescribed or optimized stability and
accuracy properties. Written primarily in
MATLAB and makes use of the Optimization
Toolbox and Global Optimization Toolbox, as
well as CVX.
Documentation
Code
Paper
A software laboratory for designing,
analyzing, and testing numerical methods for
initial value ODEs, written in Python. NodePy
takes an object-oriented approach, in which the
basic object is a numerical ODE solver (e.g., a
Runge-Kutta or linear multistep method).
Documentation
Code
Paper
A software laboratory for designing,
analyzing, and testing numerical methods for
initial value ODEs, written in Python. NodePy
takes an object-oriented approach, in which the
basic object is a numerical ODE solver (e.g., a
Runge-Kutta or linear multistep method).
© 2011 David Ketcheson and Matteo Parsani
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