SimTech EXC 2075 Project Network 6 "Machine learning for simulation"
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241 to 250 of 877 Results
toy-models-plots.ipynb
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
Jupyter Notebook - 77.9 KB - MD5: 64985de605105262d97f2e9e4a4a8556
toy_models.py
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
Python Source Code - 18.4 KB - MD5: 4fc8b8900e22952ac96c1f2a77635b92
training_hybrid.py
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
Python Source Code - 12.3 KB - MD5: 3f8043e07e594648e098a3b5045aee5d
training_lstm.py
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
Python Source Code - 10.3 KB - MD5: ae4b05e3488f75cf7a425a65ab988d7b
training.py
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
Python Source Code - 975 B - MD5: bdfcdc29a422859173711c627dc6878e
training_toy_models.py
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
Python Source Code - 21.3 KB - MD5: 933984ef65287c1314b907e614c6067c
unite_toolbox.zip
Apr 9, 2025 - Replication Data for: An entropy-based evaluation of conceptual constraints in hybrid hydrological models
ZIP Archive - 18.4 KB - MD5: 934d31b87c9e5e48157bfa0734edaea9
PN 6-8 (II)(Universität Stuttgart)
Mar 28, 2025
Spatiotemporal ensembles often result from physical simulations. These ensembles contain many information-rich members, each corresponding to different simulation input parameters. The extensive data size makes manual analysis infeasible, necessitating automated approaches to assist the analysis. In the preceding project (PN 6-8 (I)), methods and t...
PN 6-15(Universität Stuttgart)
Nov 20, 2024
This Dataverse contains replication data and visualizations from Project Network 6-15: Machine Learning and Reservoir Computing with Many-Body Dynamics. The project is part of SimTech’s Project Network 6, Machine Learning for Simulation (https://www.simtech.uni-stuttgart.de/exc/research/pn/pn6/ ). The Dataverse includes simulations of non-equilibri...
Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]
Nov 5, 2024 - PN 6-3
Holzmüller, David; Grinsztajn, Léo; Steinwart, Ingo, 2024, "Code and Data for: Better by default: Strong pre-tuned MLPs and boosted trees on tabular data [NeurIPS, arXiv v2]", https://doi.org/10.18419/DARUS-4555, DaRUS, V1
This dataset contains code and data for our paper "Better by default: Strong pre-tuned MLPs and boosted trees on tabular data", specifically, the NeurIPS version which is also the second version on arXiv. The main code is provided in pytabkit_code.zip and contains further documentation in README.md and the docs folder. The main code is also provide...
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