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  • šŸ‘‹ Hi, Iā€™m @cmarcotte
  • šŸ‘€ Iā€™m interested in computational physics, pattern formation, dynamical systems, high-performance computing, computational efficiency, and frequently where these topics intersect with cardiac dynamics.
  • šŸŒ± Iā€™m currently learning about Global Sensitivity Analysis methods.
  • šŸ’žļø Iā€™m looking to collaborate on high-performance computing approaches to novel cardiac problems.
  • šŸ“« How to reach me: at "christopher dot marcotte at durham dot ac dot uk".

Christopher Marcotte's Projects

cgle icon cgle

A set of functions for use in solving the complex Ginzburg-Landau equation.

ctesn icon ctesn

Implementation of some basic ideas around continuous-time Echo State Networks (CTESN) in julia

dataassim.jl icon dataassim.jl

Implementation of various ensemble Kalman Filter data assimilation methods in Julia

dedalus icon dedalus

A flexible framework for solving PDEs with modern spectral methods.

dedalus_fhn_quenching icon dedalus_fhn_quenching

Code for the investigation of quenching of stable pulses in FitzHugh-Nagumo using the Dedalus Project

fenkar_fittings icon fenkar_fittings

FInding local minima for model parameters in the Fenton-Karma model.

fenkar_parameterization icon fenkar_parameterization

Simple repository for the tuning of model parameters in the Fenton-Karma model using varied optimization strategies.

fenkar_program icon fenkar_program

OpenMP parallelized 3D Fenton-Karma finite difference code. Not well optimized, but functions.

filamentdetangling icon filamentdetangling

Repository for code for detangling sets of unordered points into a finite set of scroll wave filaments

frontquenching icon frontquenching

Quenching stable structures in a front model using perturbations to the stable state.

linear_prediction icon linear_prediction

Simple Python code and shell driver for the prediction of critical quenching perturbations

mdc_test icon mdc_test

Minimally Disruptive Curves Test for cardiac models

nn_interpolant icon nn_interpolant

Short script in Julia which compares the performance of a neural network interpolant to a simple surrogate model for critical quenching prediction.

puttingthedatainda icon puttingthedatainda

Some scripts for interpreting data and observation files for the purposes of feeding experimental data into our cardiacDA project.

pyplot-paper-shortcuts icon pyplot-paper-shortcuts

A simple repository with set-up code for creating publication-matching figures in matplotlib, using Python and Julia.

readrestarts icon readrestarts

Repository for reading restart, observation, and tips files for the cardiacDA project.

slicedrd icon slicedrd

Implements translational symmetry-reduction by means of the first Fourier mode phase constraint.

timeseriesprediction icon timeseriesprediction

Some baseline efficacies for time-series prediction of single-cell cardiac action potentials.

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