June 12, 2020 Software Open Access

Olivier Pannekoucke

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nmm##2200000uu#4500</leader>
  <controlfield tag="005">20210317064229.0</controlfield>
  <controlfield tag="001">3891101</controlfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">1825331</subfield>
    <subfield code="z">md5:7e8d339f19c1baea8b9f51e658f0ca65</subfield>
    <subfield code="u">https://zenodo.org/record/3891101/files/opannekoucke/pdenetgen-1.0.1.zip</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2020-06-12</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">software</subfield>
    <subfield code="o">oai:zenodo.org:3891101</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">INPT-ENM, UMR CNRS CNRM 3589, CERFACS</subfield>
    <subfield code="0">(orcid)0000-0002-3249-2818</subfield>
    <subfield code="a">Olivier Pannekoucke</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">opannekoucke/pdenetgen: pde-netgen-GMD</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">http://www.cecill.info/licences/Licence_CeCILL-B_V1-en.html</subfield>
    <subfield code="a">CeCILL-B Free Software License Agreement</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The later exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally-efficient yet compact representations to address a variety of issues, including among others adjoint derivation, model calibration, forecasting, data assimilation as well as uncertainty quantification.&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Olivier Pannekoucke and Ronan Fablet. &amp;quot;&lt;a href="https://doi.org/10.5194/gmd-2020-35"&gt;PDE-NetGen 1.0: from symbolic PDE representations of physical processes to trainable neural network representations&lt;/a&gt;&amp;quot;, Geoscientific Model Development (2020) https://doi.org/10.5194/gmd-2020-35&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Description of the version&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Version of the package based on tensorflow.keras, where neural network can be generated by using &lt;code&gt;TrainableScalar&lt;/code&gt; or exogenous network.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Examples&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;As an illustration, the workflow is first presented for the 2D diffusion equation, then applied to the data-driven and physics-informed identification of uncertainty dynamics for the Burgers equation.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">url</subfield>
    <subfield code="i">isSupplementTo</subfield>
    <subfield code="a">https://github.com/opannekoucke/pdenetgen/tree/1.0.1</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.3891100</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.3891101</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">software</subfield>
  </datafield>
</record>