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        <datestamp>2025-05-22T07:51:14Z</datestamp>
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            <creator>
              <creatorName>He, Yi</creatorName>
              <givenName>Yi</givenName>
              <familyName>He</familyName>
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            <creator>
              <creatorName>Xue, Xiao</creatorName>
              <givenName>Xiao</givenName>
              <familyName>Xue</familyName>
              <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org">0000-0002-1193-463X</nameIdentifier>
            </creator>
            <creator>
              <creatorName>Hu, Yukun</creatorName>
              <givenName>Yukun</givenName>
              <familyName>Hu</familyName>
            </creator>
            <creator>
              <creatorName>Yang, Yiming</creatorName>
              <givenName>Yiming</givenName>
              <familyName>Yang</familyName>
            </creator>
            <creator>
              <creatorName>Cheng, Xiaoyuan</creatorName>
              <givenName>Xiaoyuan</givenName>
              <familyName>Cheng</familyName>
            </creator>
            <creator>
              <creatorName>Wang, Hai</creatorName>
              <givenName>Hai</givenName>
              <familyName>Wang</familyName>
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          <titles>
            <title><![CDATA[Benchmark dataset Turbulent Channel Flow for Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction]]></title>
          </titles>
          <subjects>
            <subject>Turbulent flows</subject>
            <subject>Ordinary differential equations, difference equations and dynamical systems</subject>
            <subject>Computational methods in fluid flow, heat and mass transfer (incl. computational fluid dynamics)</subject>
            <subject>Deep learning</subject>
            <subject>Machine learning not elsewhere classified</subject>
            <subject>nonlinear dynamic behavior</subject>
            <subject>chaos sequence</subject>
            <subject>benchmark dataset</subject>
            <subject>AI4Science</subject>
          </subjects>
          <dates>
            <date dateType="Created">2025-05-27</date>
            <date dateType="Updated">2025-05-27</date>
          </dates>
          <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
          <publicationYear>2025</publicationYear>
          <publisher>University College London</publisher>
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            <description descriptionType="Abstract"><![CDATA[<p dir="ltr">This dataset serves as a benchmark for 3D turbulent channel flows, based on simulations performed using a high-fidelity lattice Boltzmann method (LBM) solver, as described in Xue et al., Phys. Fluids, 34,5, 2022.</p><p dir="ltr">It comprises 240 trajectories generated from 3D periodic turbulent channel flow simulations with a fixed relaxation time, $\tau = 0.5025$. We extract the central cross-section of the domain along the streamwise ($x$) direction with 3 coordinate components. The spatial resolution is $192 \times 192$, and the<b> friction Reynolds number</b> is set to $Re_{\tau} = 180$, equivalent to $Re = 3250$. The dataset is split into 192 training, 24 validation, and 24 test trajectories, all provided in <i>.npy</i> format.</p><p dir="ltr">This dataset is designed to facilitate machine learning research in dynamical systems, especially in the challenging context of high-dimensional, turbulent flow regimes.</p>]]></description>
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