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      <h1 class="project-name">Learn2Engineer</h1>
      <h2 class="project-tagline">Tackling Engineering Challenges with AI</h2>
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      <b>Important Note:</b> This is a preview for a submitted workshop proposal for NeurIPS 2024.
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      <h2>Abstract</h2>
      <p>    Engineering is a broad discipline with a variety of computational challenges. Despite having great potential to improve simulation and support design, engineering applications have received little attention in the machine learning community. Engineering problems can be highly specialised and hard to incorporate into standard machine learning models which yields intriguing requirements for machine learning involving processing complex geometries, incorporating physical compatibility and requiring high sample efficiency. In contrast to previous workshops that addressed the broader topic of AI for science, we explicitly focus on engineering.</p>

      <h2>Submissions</h2>
      We accept contributions of four page technical papers. Specifically, we are interested in contributions that address the following topics in the context of machine learning/deep learning and engineering: data-driven simulation, design optimisation, data-centric or active learning, foundation models, datasets and benchmarks, applications in production environments, uncertainty quantification and explainability.

      <h2>Schedule</h2>
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          <td>Time</td>
          <td>Session</td>
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          <td>9:00</td>
          <td>Start of workshop...</td>
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      <h2>Invited Speakers</h2>
      t.b.a

      <h2>Organizers</h2>
      <ul>
        <li>Johannes Brandstetter, University of Linz</li>
        <li>Jan van Delden, University of Göttingen</li>
        <li>Ashish Kapoor, Scaled Foundations</li>
        <li>Olga Fink, EPFL Lausanne</li>
        <li>Timo Lüddecke, University of Göttingen</li>
        <li>Nils Thuerey, TU Munich</li>
      </ul>


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