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CITATION.cff
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# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: >-
proFit: Probabilistic Response Model Fitting with
Interactive Tools
message: >-
If you use this software, please cite it using the
metadata from this file. To cite a specific version of
DESC, please cite the correct version from Zenodo at
https://zenodo.org/search?page=1&size=20&q=conceptrecid:%223580488%22&sort=-version&all_versions=True
type: software
license: MIT
authors:
- given-names: Christopher
family-names: ' Albert'
affiliation: Technische Universität Graz
orcid: 'https://orcid.org/0000-0003-4773-416X'
email: [email protected]
- given-names: Maximilian
family-names: Kendler
affiliation: Technische Universität Graz
- given-names: Robert
family-names: Babin
affiliation: Technische Universität Graz
- given-names: Michael
family-names: Hadwiger
affiliation: Technische Universität Graz
- given-names: Richard
family-names: Hofmeister
affiliation: Helmholtz-Zentrum Geesthacht
- given-names: Manal
family-names: Khallaayoune
affiliation: Max-Planck-Institut für Plasmaphysik
- given-names: Francesco
family-names: Kramp
affiliation: Technische Universität Graz
- given-names: Katharina
family-names: Rath
affiliation: Max-Planck-Institut für Plasmaphysik
orcid: 'https://orcid.org/0000-0002-4962-5656'
- given-names: Baptiste
family-names: Rubino-Moyner
affiliation: Max-Planck-Institut für Plasmaphysik
identifiers:
- type: doi
value: 10.5281/zenodo.3580488
description: >-
Main DOI, represents all versions and resolves to the
latest one.
repository-code: 'https://github.com/redmod-team/profit'
url: 'https://profit.readthedocs.io/'
keywords:
- Parameter Study
- Gaussian Process
- Regression
- HPC
- Active Learning
abstract: >-
<p>proFit is a collection of tools for studying parametric
dependencies of black-box simulation codes or experiments
and construction of reduced order response models over
input parameter space.</p><p>proFit can be fed with a
number of data points consisting of different input
parameter combinations and the resulting output of the
simulation under investigation. It then fits a
response-surface through the point cloud using Gaussian
process regression (GPR) models. This probabilistic
response model allows to predict (interpolate) the output
at yet unexplored parameter combinations including
uncertainty estimates. It can also tell you where to put
more training points to gain maximum new information
(experimental design) and automatically generate and start
new simulation runs locally or on a cluster. Results can
be explored and checked visually in a web
frontend.</p><p>Telling proFit how to interact with your
existing simulations is easy and requires no changes in
your existing code. Current functionality covers starting
simulations locally or on a cluster via <a
href=\"https://slurm.schedmd.com\">Slurm</a>, subsequent
surrogate modelling using <a
href=\"https://github.com/SheffieldML/GPy\">GPy</a>, <a
href=\"https://github.com/scikit-learn/scikit-learn\">scikit-learn</a>,
as well as an active learning algorithm to iteratively
sample at interesting points and a
Markov-Chain-Monte-Carlo (MCMC) algorithm. The web
frontend to interactively explore the point cloud and
surrogate is based on <a
href=\"https://github.com/plotly/dash\">plotly/dash</a>.</p><p>Features
include: <ul><li>Compute evaluation points (e.g. from a
random distribution) to run simulation</li><li>Template
replacement and automatic generation of run
directories</li><li>Starting parallel runs locally or on
the cluster (SLURM)</li><li>Collection of result output
and postprocessing</li><li>Response-model fitting using
Gaussian Process Regression and Linear
Regression</li><li>Active learning to reduce number of
samples needed</li><li>MCMC to find a posterior parameter
distribution (similar to active
learning)</li><li>Graphical user interface to explore the
results</li></ul></p>",