PN 6

SimTech EXC 2075 Project Network 6 "Machine learning for simulation"

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11 to 20 of 26 Results

PN 6-3(Institute for Stochastics and Applications, Universität Stuttgart) Mar 16, 2021 SimTech Project PN6-3 "Understanding Physical Constraints in Machine Learning for Simulation"
PN 6-4(Universität Stuttgart) Oct 27, 2020 SimTech Project PN 6-4 "Visual Analytics for Deep Learning"
PN 6-6(Universität Stuttgart) Sep 29, 2021 SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"
PN 6A-3(Universität Stuttgart) Mar 8, 2024 Advanced learning strategies for potential energy surfaces applied to organic electrolytes
PN 6A-4(Universität Stuttgart) Mar 8, 2024 Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory
Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials Feb 20, 2023 Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1 Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint...
PyPlant: A Python Framework for Cached Function Pipelines Jan 24, 2022 - PN 6-6 Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/darus-2249, DaRUS, V1 PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed.
Replication Code for: Meta-Uncertainty in Bayesian Model Comparison May 25, 2023 - Data and Code for: Meta-Uncertainty in Bayesian Model Comparison Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/darus-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF] This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper)....
Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity" Oct 5, 2021 - PN 6-6 Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1 We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.
Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework Mar 29, 2022 Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/darus-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF] This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which...

PN 6-3(Institute for Stochastics and Applications, Universität Stuttgart)

Mar 16, 2021

SimTech Project PN6-3 "Understanding Physical Constraints in Machine Learning for Simulation"

PN 6-4(Universität Stuttgart)

Oct 27, 2020

SimTech Project PN 6-4 "Visual Analytics for Deep Learning"

PN 6-6(Universität Stuttgart)

Sep 29, 2021

SimTech Project PN 6-6 "Machine Learning for Data-driven Visualization"

PN 6A-3(Universität Stuttgart)

Mar 8, 2024

Advanced learning strategies for potential energy surfaces applied to organic electrolytes

PN 6A-4(Universität Stuttgart)

Mar 8, 2024

Unified diagnostic evaluation of physics-based, data-driven and hybrid hydrological models based on information theory

Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials

Feb 20, 2023

Zaverkin, Viktor; Holzmüller, David; Bonfirraro, Luca; Kästner, Johannes, 2023, "Pre-trained and fine-tuned ANI models for: Transfer learning for chemically accurate interatomic neural network potentials", https://doi.org/10.18419/darus-3299, DaRUS, V1

Pre-trained and fine-tuned ANI models using the Gaussian Moments Neural Network (GM-NN) approach. Code for GM-NN implemented within the Tensorflow framework, including the respective documentation and tutorials, can be found on GitLab. The data represents TensorFlow v2 checkpoint...

PyPlant: A Python Framework for Cached Function Pipelines

Jan 24, 2022 - PN 6-6

Tkachev, Gleb, 2022, "PyPlant: A Python Framework for Cached Function Pipelines", https://doi.org/10.18419/darus-2249, DaRUS, V1

PyPlant is a simple coroutine-based framework for writing data processing pipelines. PyPlant's goal is to simplify caching of intermediate results in the pipeline and avoid re-running expensive early stages of the pipeline, when only the later stages have changed.

Replication Code for: Meta-Uncertainty in Bayesian Model Comparison

May 25, 2023 - Data and Code for: Meta-Uncertainty in Bayesian Model Comparison

Schmitt, Marvin, 2023, "Replication Code for: Meta-Uncertainty in Bayesian Model Comparison", https://doi.org/10.18419/darus-3514, DaRUS, V1, UNF:6:zUDr3KGdcaDCy+jFtcz8lA== [fileUNF]

This dataverse contains the code for the paper Meta-Uncertainty in Bayesian Model Comparison: https://doi.org/10.48550/arXiv.2210.07278 Note that the R code is structured as a package, thus requiring a local installation with subsequent loading via library(MetaUncertaintyPaper)....

Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"

Oct 5, 2021 - PN 6-6

Tkachev, Gleb, 2021, "Replication Data for: "S4: Self-Supervised learning of Spatiotemporal Similarity"", https://doi.org/10.18419/darus-2174, DaRUS, V1

We train a self-supervised siamese model that enables querying for similar behavior on spatiotemporal volumes. Here we provide the code and data needed to reproduce the representative figures of the paper. See the notes and the included readme file for details.

Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework

Mar 29, 2022

Holzmüller, David, 2022, "Replication Data for: Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework", https://doi.org/10.18419/darus-1779, DaRUS, V1, UNF:6:aIyuHfDcWPT9LJvtkCge9w== [fileUNF]

This dataset contains supplementary code for the paper Fast Sparse Grid Operations using the Unidirectional Principle: A Generalized and Unified Framework. The code is also provided on GitHub. Here, we additionally provide the runtime measurement data generated by the code, which...

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