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NN-OpInf
============= 
NN-OpInf is a PyTorch based approach to operator inference that utilizes composable, structure preserving neural networks to represent nonlinear operators. Operator inference, as made popular by `Peherstorfer, Willcox, and co-authors <https://willcox-research-group.github.io/rom-operator-inference-Python3/source/opinf/intro.html>`_, is an approach for inferring low-dimensional systems from data. Classically, OpInf infers polynomial models for the system dynamics. Numerous systems of interest, however, do not admit such polynomial structure. NN-OpInf addresses this challenge by parameterizing operators with neural networks.  
 
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    <div class="nnopinf-cards">
        <div class="nnopinf-card">
            <h2><a href="getting_started.html">Getting Started</a></h2>
            <p>Install the package and train your first model.</p>
        </div>
        <div class="nnopinf-card">
            <h2><a href="api.html">API Reference</a></h2>
            <p>Browse operators, models, steppers, and training utilities.</p>
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        <div class="nnopinf-card">
            <h2><a href="examples.html">Examples</a></h2>
            <p>Follow end-to-end workflows and compare ROM outputs.</p>
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.. toctree::
    :maxdepth: 1
    :hidden:

    getting_started
    api
    examples