WueData (Univ Würzburg)
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DynaBench: Gas-Dynamics Cloud Medium
No full textThis package contains the Gas-Dynamics equation simulations for a medium number of scattered (non-grid) observation locations. It is part of the DynaBench dataset
Neural representation of goal direction in the monarch butterfly brain
No full textRaw data and matlab scripts used for the manuscript: 'Neural representation of goal direction in the monarch butterfly brain' published in Nature Communications (2023). Please note, for optimal use of the raw data please read the 'readme' file in advance
DynaBench: A benchmark dataset for learning dynamical systems from low-resolution data
No full textThis is the meta-repository for the DynaBench dataset - a benchmark dataset for learning physical systems from low-resolution, non-grid data. The benchmark contains simulations of several physical systems (advection, burgers', gas dynamics, kuramoto-sivashinsky, reaction-diffusion, wave). Each system has been simulated several times using a high-resolution numerical solver, from which the observations have been sampled at different resolutions (low, medium, high) as well as two different spatial structures (grid, scattered).The dataset is split into 42 parts (6 equations x 7 combinations of resolution/structure). Each part can be downloaded separately and contains 7000 simulations of the given equation at the given resolution and structure. The simulations are grouped into chunks of 500 simulations saved in the hdf5 file format. Each chunk contains the variable "data", where the values of the simulated system are stored, as well as the variable "points", where the coordinates at which the system has been observed are stored. For more details visit the DynabBench website at https://professor-x.de/dynabench/. The dataset is best used as part of the dynabench python package available at https://pypi.org/project/dynabench/.Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at https://professor-x.de/dynabench
DynaBench: Advection Grid Low
No full textThis package contains the Advection equation simulations for a medium number of structured (grid) observation locations. It is part of the DynaBench dataset
DynaBench: Burgers Grid High
No full textThis package contains the Burgers' equation simulations for a high number of structured (grid) observation locations. It is part of the DynaBench dataset
DynaBench: Kuramoto-Sivashinsky Grid Full
No full textThis package contains the Kuramoto-Sivashinsky equation simulations for the full high-resolution structured (grid) observation locations. It is part of the DynaBench dataset
DynaBench: Reaction-Diffusion Grid Medium
No full textThis package contains the Reaction-Diffusion equation simulations for a medium number of structured (grid) observation locations. It is part of the DynaBench dataset
DynaBench: Wave Grid Low
No full textThis package contains the Wave equation simulations for a low number of structured (grid) observation locations. It is part of the DynaBench dataset
Volltext- und Erschließungsdaten zur Handschrift UBW, M.ch.f.834 ("Würzburger Wundarznei")
No full textDas Datenpaket umfasst die im Rahmen eines bibliotheksinternen Erschließungs- und Auszeichnungsprojekts im Jahr 2023 systematisch überarbeitete Fassung der Kompletttranskription der Handschrift UBW, M.ch.f.834 („Würzburger Wundarznei“) sowie eine finalisierte Fassung der Erschließungsdaten der Handschrift
DynaBench: Gas-Dynamics Grid Low
No full textThis package contains the Gas-Dynamics equation simulations for a low number of structured (grid) observation locations. It is part of the DynaBench dataset