181 to 190 of 1,643 Results
Mar 15, 2025 - Model comparison for LTNE processes in porous media - conduction
Kostelecky, Anna Mareike, 2025, "DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"", https://doi.org/10.18419/DARUS-4781, DaRUS, V1
This dataset contains the source code to reproduce the simulations for the dual network model presented in Kostelecky et al., Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects, International Journal of Heat and Mass Transfer. \TODO: add doi after acceptance. Files README.md: Instruction about installati... |
Mar 15, 2025 -
DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
XZ Archive - 5.8 KB -
MD5: 266936cbce51808dcf4d1f975bb8fe32
All files related to installation of code via Docker. |
Mar 15, 2025 -
DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
Python Source Code - 4.9 KB -
MD5: cec0d7ef54445a987cd6f19bdf1b1723
Installation script for dumux module `Kostelecky2025a` and all related dumux and dune modules. |
Mar 15, 2025 -
DuMuX code for dual network for: "Local Thermal Non-Equilibrium Models in Porous Media: A Comparative Study of Conduction effects"
Markdown Text - 6.7 KB -
MD5: 458753947a320db45be4fc735f0e6346
Instructions to install and run simulations. |
Mar 4, 2025 - D03: Development and realisation of validation benchmarks
Kohlhaas, Rebecca; Morales Oreamuno, Maria Fernanda; Lacheim, Alina, 2025, "BayesValidRox 2.0.0", https://doi.org/10.18419/DARUS-4752, DaRUS, V1
Release 2.0.0 of BayesValidRox. BayesValidRox is an open-source python package that provides methods for surrogate modeling, Bayesian inference and model comparison. (2025-02-05) |
Mar 4, 2025 -
BayesValidRox 2.0.0
Gzip Archive - 142.8 KB -
MD5: b8a1b0a6df6184dd1cc2d58e8171026e
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Feb 28, 2025 - Analytic Computing
Fathallah, Nadeen, 2025, "Code for Improving Video Caption Accuracy with LLMs", https://doi.org/10.18419/DARUS-4776, DaRUS, V1
As part of the IKILeUS project at the University of Stuttgart, research was conducted to explore how Large Language Models (LLMs) can enhance the accuracy and contextual relevance of automatic speech recognition (ASR)-generated captions. While ASR tools provide a foundation for accessibility, they often produce grammatical errors, misinterpret homo... |
Feb 28, 2025 -
Code for Improving Video Caption Accuracy with LLMs
Jupyter Notebook - 7.0 KB -
MD5: 5e884e1584616b21d000203d27683c3a
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Feb 28, 2025 -
Code for Improving Video Caption Accuracy with LLMs
Jupyter Notebook - 19.4 KB -
MD5: c24b839b69635b4bf4b11a386188bed3
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Feb 28, 2025 -
Code for Improving Video Caption Accuracy with LLMs
Jupyter Notebook - 11.9 KB -
MD5: f6e97b6eeb114cd95822523bbc65ad9c
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