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Pavol Jusko / CCIT
BSD 3-Clause "New" or "Revised" LicensePrograms for controlling CAS Cryogenic Ion Trap
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Provide examples and guidance on how to serve and fine-tune LLMs at the MPCDF.
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Beata Turonova / GAPSTOP_TM
GNU General Public License v3.0 or laterGPU-Accelerated Python STOPgap for Template Matching is an open-source SW for fast template matching (TM) in cryo electron tomograms that is based on TM routines from STOPGAP.
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Martin Reinecke / ducc
Affero General Public License v1.0Updated -
Dioscuri-Biophysics / GlycoSHIELD-MD
GNU General Public License v3.0 or laterSource code of GlycoSHIELD | GlycoSHIELD WebApp https://dioscuri-biophysics.pages.mpcdf.de/glycoshield-md/
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Paul Nuehrenberg / TrackUtil
MIT LicenseVideo annotation and interactive post-processing of animal trajectories
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mpdl-bloxberg / faucet
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Tobias Winchen / psrdada_cpp
MIT LicenseUpdated -
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MPIfR-BDG / psrdada_cpp
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Tobias Glaubach / mke_clientlib
GNU General Public License v3.0 or laterMeerKAT Extension (MKE) (r)emote (i)nterface (m)anagement (lib)rary interface library for accessing remote experiment and analysis data in a dbserver
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GUI interface between tokamak data and transport codes. Developed first for the ASDEX-Upgrade shotfile system, it can now read IMAS data as input.
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Constantin Gahr / Learning physics-based reduced models from data for the Hasegawa-Wakatani equations
BSD 2-Clause "Simplified" LicenseCURRENTLY UNDER CONSTRUCTION! This paper focuses on the construction of non-intrusive Scientific Machine Learning (SciML) Reduced-Order Models (ROMs) for nonlinear, chaotic plasma turbulence simulations. In particular, we propose using Operator Inference (OpInf) to build low-cost physics-based ROMs from data for such simulations. As a representative example, we focus on the Hasegawa-Wakatani (HW) equations used for modeling two-dimensional electrostatic drift-wave plasma turbulence. For a comprehensive perspective of the potential of OpInf to construct accurate ROMs for this model, we consider a setup for the HW equations that leads to the formation of complex, nonlinear and self-driven dynamics, and perform two sets of experiments. We first use the data obtained via a direct numerical simulation of the HW equations starting from a specific initial condition and train OpInf ROMs for predictions beyond the training time horizon. In the second, more challenging set of experiments, we train ROMs using the same data set as before but this time perform predictions for six other initial conditions. Our results show that the OpInf ROMs capture the important features of the turbulent dynamics and generalize to new and unseen initial conditions while reducing the evaluation time of the high-fidelity model by up to six orders of magnitude in single-core performance. In the broader context of fusion research, this shows that non-intrusive SciML ROMs have the potential to drastically accelerate numerical studies, which can ultimately enable tasks such as the design and real-time control of optimized fusion devices.
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Provide examples and guidance on how to serve and fine-tune LLMs at the MPCDF.
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This repository is a binder repo for a collaborative usable setup for jupyterlab and pyiron environment
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