June 12, 2020 Software Open Access

Olivier Pannekoucke

DCAT Export

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        <foaf:name>Olivier Pannekoucke</foaf:name>
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    <dct:title>opannekoucke/pdenetgen: pde-netgen-GMD</dct:title>
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    <dct:description>&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;</dct:description>
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