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partnerships.html
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<!DOCTYPE html>
<html lang="en">
<head>
<!-- Required meta tags -->
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1, shrink-to-fit=no"
/>
<!-- Bootstrap Imports -->
<link
href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"
rel="stylesheet"
/>
<script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>
<link rel="stylesheet" href="https://unpkg.com/[email protected]/dist/aos.css" />
<link rel="stylesheet" href="./style.css" />
<link rel="stylesheet" href="./infostyle.css" />
<link rel="stylesheet" href="./panelstyle.css" />
<link rel="icon" href="./logos/eq.ico" type="image/x-icon" />
<script src="svg-inject.min.js"></script>
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<link
rel="stylesheet"
href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css"
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<meta property="og:site_name" content="equadratures" />
<meta property="og:type" content="website" />
<link rel="image_src" href="logos/figure.png" />
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<meta property="twitter:image" content="logos/figure.png" />
<meta property="og:url" content="https://equadratures.org/" />
<meta
property="og:title"
content="Learn continuous structure | equadratures"
/>
<meta
property="og:description"
content="equadratures is an open-source python framework for fitting models to data. It may be used for polynomial regression, sparse polynomial approximation, subspace-based dimension reduction, polynomial chaos, and more recently for kernel-based learning. Underneath the hood it leverages numerical quadrature and orthogonal polynomials."
/>
<meta property="og:image" content="./icons/logo_new.png" />
<meta property="og:image:url" content="./icons/logo_new.png" />
<meta property="og:image:secure_url" content="./icons/logo_new.png" />
<meta name="twitter:card" content="summary_large_image" />
<meta
name="twitter:description"
content="equadratures is an open-source python framework for fitting models to data. It may be used for polynomial regression, sparse polynomial approximation, subspace-based dimension reduction, polynomial chaos, and more recently for kernel-based learning. Underneath the hood it leverages numerical quadrature and orthogonal polynomials."
/>
<meta name="twitter:site" content="@equadratures" />
<meta
name="twitter:title"
content="Learn continuous structure | equadratures"
/>
<meta name="twitter:creator" content="@equadratures" />
<meta name="twitter:url" content="https://twitter.com/equadratures" />
<title>Partnerships | equadratures</title>
<meta
name="description"
content="equadratures is an open-source python framework for fitting models to data. It may be used for polynomial regression, sparse polynomial approximation, subspace-based dimension reduction, polynomial chaos, and more recently for kernel-based learning. Underneath the hood it leverages numerical quadrature and orthogonal polynomials."
/>
<meta
name="keywords"
content="data science, machine learning, polynomial chaos, uncertainty quantification, model auditing, Pranay Seshadri"
/>
</head>
<body class="body light-mode">
<div id="header"></div>
<main data-aos="fade-up">
<div class="d-flex justify-content-center p-4">
<h1>Partnerships & Funding</h1>
</div>
<div class="col-sm justify-content-center p-4">
<h2>
equadratures has been supported by numerous grants. It was founded
through an
<a href="https://epsrc.ukri.org/"
>Engineering and Physical Sciences Research Council (EPSRC)
Knowledge Transfer Fellowship (KTF)</a
>
from the
<a href="https://www.eng.cam.ac.uk/"
>Department of Engineering at the University of Cambridge</a
>
in 2016. Since 2018, the project has been supported by Wave 1 of The
<a href="https://www.ukri.org/">UKRI</a> Strategic Priorities Fund
under the EPSRC Grant EP/T001569/1, particularly the Digital twins for
aeronautics theme within that grant, and by the
<a
href="https://www.turing.ac.uk/research/research-programmes/data-centric-engineering"
>Lloyd’s Register Foundation-Alan Turing Institute programme on
Data-Centric Engineering</a
>
under the LRF grant G0095. The project is also grateful to the
financial support of
<a href="https://www.jesus.cam.ac.uk/">Jesus College Cambridge </a>
and the
<a href="https://www.cambridgetrust.org/">Cambridge Trusts</a>. The
outputs of all these projects have been made openly available.
</h2>
<center>
<div class="row">
<div class="column_type_3">
<img
src="./logos/epsrc.png"
style="width: 30%; padding: 1px; max-width: 250px"
/>
<img
src="./logos/turing.png"
style="width: 30%; padding: 1px; max-width: 250px"
/><br />
<img
src="./logos/jesus.png"
style="width: 30%; padding: 1px; max-width: 250px"
/>
<img
src="./logos/cambridge.png"
style="width: 30%; padding: 1px; max-width: 250px"
/><br />
<img
src="./logos/lrf.png"
style="width: 30%; padding: 1px; max-width: 250px"
/>
</div>
</div>
</center>
</div>
<div id="footer"></div>
</main>
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</body>
</html>