Stuttgart Center for Simulation Science (SC SimTech)

EXC 2075 Project Networks

SIGDIUS

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4,671 to 4,680 of 25,451 Results

test_model_wrapper.py May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics Python Source Code - 4.8 KB - MD5: d0a9867c442111e30c0e12e53092ca8e
test_predict_cli.py May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics Python Source Code - 9.1 KB - MD5: 49c9616d14d08a836de518a167eeacf0
test_pretrained_validation.py May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics Python Source Code - 6.2 KB - MD5: 142b8a6fe1fd2fcc3682f70c93812076
test_randomized_models.py May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics Python Source Code - 11.9 KB - MD5: 9588c213a051bf937b266de6312ed302
utils.py May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics Python Source Code - 1023 B - MD5: 7b087deadf47c713a62105c64888140a
utils.py May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics Python Source Code - 1023 B - MD5: 7b087deadf47c713a62105c64888140a
Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart) May 20, 2026PN 6 We investigate computation through the lens of dynamical systems, unifying physical processes and machine learning. By treating both hardware and algorithms as evolving dynamical systems, we leverage natural physical dynamics - such as relaxation to stable states and phase transitions - as a foundation for robust, efficient computation. Our work sh...
charmm36-jul2022mod.ff.zip May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching" ZIP Archive - 1.1 MB - MD5: e5e758a42a7a04a3af52bd2a3746f599 Ready-to-use CHARMM36m force field with our parameters for phosphorylated aminoacids
charmm_toppar_mod.zip May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching" ZIP Archive - 1.4 MB - MD5: 10d788673880f41321b528dd43b8b042 Patched CHARMM36m force field files for CHARMM with our parameters
nmr_MD_sims.zip May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching" ZIP Archive - 19.4 GB - MD5: bd30ce5c884d7dda4d9bb1b00d847840 DATA MD sims for calculating relaxation times and Excel sheet with all datapoints, including experimental values.

test_model_wrapper.py

May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

Python Source Code - 4.8 KB -

test_predict_cli.py

May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

Python Source Code - 9.1 KB -

test_pretrained_validation.py

May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

Python Source Code - 6.2 KB -

test_randomized_models.py

May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

Python Source Code - 11.9 KB -

utils.py

May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

Python Source Code - 1023 B -

utils.py

May 22, 2026 - Trained Neural Networks on Simulated Data of Groundwater Heat Plume Characteristics

Python Source Code - 1023 B -

Studying Dynamical Systems for Intelligible Modeling and Unconventional Computing(Universität Stuttgart)

May 20, 2026PN 6

We investigate computation through the lens of dynamical systems, unifying physical processes and machine learning. By treating both hardware and algorithms as evolving dynamical systems, we leverage natural physical dynamics - such as relaxation to stable states and phase transitions - as a foundation for robust, efficient computation. Our work sh...

charmm36-jul2022mod.ff.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 1.1 MB -

Ready-to-use CHARMM36m force field with our parameters for phosphorylated aminoacids

charmm_toppar_mod.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 1.4 MB -

Patched CHARMM36m force field files for CHARMM with our parameters

nmr_MD_sims.zip

May 11, 2026 - Supplementary Material for "Refinement of CHARMM36m force field parameters for protein phosphorylation by force-matching"

ZIP Archive - 19.4 GB -

DATA

MD sims for calculating relaxation times and Excel sheet with all datapoints, including experimental values.

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