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Python Source Code - 1.6 KB - MD5: 782504a5d642c49fe06ac84454d839ae
Tabular Data - 115.1 KB - 1 Variables, 900 Observations - UNF:6:PvGqMM6W/pHZC5jT/aO/zA==
Tabular Data - 115.1 KB - 1 Variables, 900 Observations - UNF:6:iy53m0Jt/7ALpTj2ZKBjdA==
Sep 11, 2025 - PN 7-6
Kneifl, Jonas; Rettberg, Johannes; Herb, Julius, 2024, "ApHIN - Autoencoder-based port-Hamiltonian Identification Networks (Software Package)", https://doi.org/10.18419/DARUS-4446, DaRUS, V2
Software package for data-driven identification of latent port-Hamiltonian systems. Abstract Conventional physics-based modeling techniques involve high effort, e.g.~time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes reliability. To mitigate this, we present a data-driven system identification...
ZIP Archive - 259.7 KB - MD5: ba91f37393e536b182c572e8a83fb936
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