{"url":"/dataset/shred-rom","name":"SHRED-ROM","full_name":"Reduced order modeling with shallow recurrent decoder networks","description_markdown":"SHallow REcurrent Decoder-based Reduced Order Model (SHRED-ROM) is an ultra-hyperreduced order modeling framework aiming at reconstructing high-dimensional data from limited sensor measurements in multiple scenarios. Thanks to the composition of a Long-Short Term Memory network (LSTM) and a Shallow Decoder Network (SDN), SHRED-ROM is capable of\r\n\r\nReconstructing high-dimensional data (such as synthetic or video data) from sparse sensor measurements in new scenarios unseen during training, regardless of sensor placement,\r\nDealing with both physical, geometrical and time-dependent parametric dependencies, while being agnostic to the paraemter values,\r\nEstimating unknown parameters,\r\nCoping with both fixed or mobile sensors.\r\nImportantly, computational efficiency and memory usage are enhanced by reducing the dimensionality of full-order snapshots, allowing for compressive training of the networks, with minimal hyperparameter tuning and laptop-level computing.\r\n\r\n\r\nCONTENTS\r\n\r\nGoPro_video*.gif  -->  videos of flows around an oscillating cylinder at different inflow conditions\r\n\r\nKuramotoSivashinsky_data.npz  -->  time-dependent state, whose dynamics is described by the 1D Kuramoto-Sivashinsky equation, for different viscosity and initial condition frequencies. The corresponding parameters are also provided\r\n\r\nPinball_data.npz  -->  time-dependent densities, whose dynamics is described by the advection-diffusion partial differential equation, for different velocities of the rotating cylinders. The corresponding parameters and the transport term, which is computed through steady Navier-Stokes equations, are also provided\r\n\r\nFlowAroundObstacle_data.npz  -->  time-dependent velocities and pressures, whose dynamics is described in terms of unsteady Navier-Stokes equations, for different obstacle geometries, inflow intensities and constant angles of attack. The corresponding parameters are also provided\r\n\r\nFlowAroundObstacle_UnsteadyParam_data.npz  -->  time-dependent velocities and pressures, whose dynamics is described in terms of unsteady Navier-Stokes equations, for different obstacle geometries, inflow intensities and time-dependent angles of attack. The corresponding parameters are also provided\r\n\r\n*.zip  -->  mesh, sensor indices and trained neural networks for code reproducibility","description_withheld":null,"homepage":"https://doi.org/10.5281/zenodo.14524524","introduced_date":"2025-02-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/reduced-order-modeling-with-shallow-recurrent","title":"Reduced Order Modeling with Shallow Recurrent Decoder Networks","first_author":"Matteo Tomasetto","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[],"tasks":[],"languages":[],"variants":["SHRED-ROM"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}