PN 6

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

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1 to 10 of 1,126 Results

Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations Apr 7, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning Egenlauf, Patrick; Březinová, Iva; Andergassen, Sabine; Klopotek, Miriam, 2026, "Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations", https://doi.org/10.18419/DARUS-5613, DaRUS, V3 This dataset contains all relevant data to reproduce the results of the paper titled "Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations". It contains the exact time series data of the two-particle reduced density matrix (2RDM) for each parameter configuration of the parameter scan, with...
supplementary-material-benchmark-NODE-TD2RDM.pdf Apr 7, 2026 - Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations Adobe PDF - 154.3 KB - MD5: dc08273407457c0ad46e22702469d37c Benchmark comparison between the predictions of the neural ODE and the TD2RDM.
hyper-parameter-optimized-line-scan-test-metrics.tar.xz Mar 23, 2026 - Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations XZ Archive - 906.4 MB - MD5: 8f45f1013c492d36b6335c7b9fa1b3e2 Data
Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) Mar 11, 2026 - PN 6-15 Gaimann, Mario U.; Klopotek, Miriam, 2025, "Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4805, DaRUS, V2, UNF:6:C1gH8zVD6YKFF/O3ZhSaSA== [fileUNF] This repository contains raw and post-processed replication data for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025). The datasets contain physical observables recorded during non-equilibrium simulations of active matter systems (swarms) driven by an external...
2026-02-16-scs_inv_driver_rep_L63_full_driver_traj_with_burnin_driver_position_v0.20.0-138-g741985bc_traintest-narma10.tab Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) Tabular Data - 2.3 MB - 2 Variables, 102000 Observations - UNF:6:062NOJWrzXCpqDXIyizSig== DataTime Series raw NARMA-10 time series file generated from the corresponding raw Lorenz-63 driver trajectory file (with burn-in phase)
2026-02-16-scs_inv_driver_rep_L63_full_driver_traj_with_burnin_driver_position_v0.20.0-138-g741985bc_traintest.tab Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) Tabular Data - 3.5 MB - 2 Variables, 102000 Observations - UNF:6:xQBIDOMcHSiBHr+e1y7RcA== DataTime Series raw Lorenz-63 driver trajectory file (with burn-in phase) for computing the corresponding NARMA-10 time series file
agent-repulsion-driver-attraction-ensemble-100-seeds.tar.xz Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) XZ Archive - 586.4 KB - MD5: 261740e44766de935bc1219cc93b8067 Data
agent-repulsion-driver-attraction.tar.xz Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) XZ Archive - 14.0 GB - MD5: eecce91d91f27b460fb779eac3eed427 Data
agent-repulsion-driver-repulsion-ensemble-100-seeds.tar.xz Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) XZ Archive - 589.1 KB - MD5: f80fa83a0b81b8501a67ebcbc8a51be3 Data
agent-repulsion-driver-repulsion.tar.xz Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025) XZ Archive - 14.7 GB - MD5: 2becad6ece957a49b3b4ccfcf487e6fd Data

Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations

Apr 7, 2026 - Coarse-grained non-equilibrium dynamics with generative machine learning

Egenlauf, Patrick; Březinová, Iva; Andergassen, Sabine; Klopotek, Miriam, 2026, "Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations", https://doi.org/10.18419/DARUS-5613, DaRUS, V3

This dataset contains all relevant data to reproduce the results of the paper titled "Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations". It contains the exact time series data of the two-particle reduced density matrix (2RDM) for each parameter configuration of the parameter scan, with...

supplementary-material-benchmark-NODE-TD2RDM.pdf

Apr 7, 2026 - Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations

Adobe PDF - 154.3 KB -

Benchmark comparison between the predictions of the neural ODE and the TD2RDM.

hyper-parameter-optimized-line-scan-test-metrics.tar.xz

Mar 23, 2026 - Replication Data for: Capturing reduced-order quantum many-body dynamics out of equilibrium via neural ordinary differential equations

XZ Archive - 906.4 MB -

Data

Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)

Mar 11, 2026 - PN 6-15

Gaimann, Mario U.; Klopotek, Miriam, 2025, "Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)", https://doi.org/10.18419/DARUS-4805, DaRUS, V2, UNF:6:C1gH8zVD6YKFF/O3ZhSaSA== [fileUNF]

This repository contains raw and post-processed replication data for the publication "Optimal information injection and transfer mechanisms for active matter reservoir computing" (Gaimann and Klopotek, 2025). The datasets contain physical observables recorded during non-equilibrium simulations of active matter systems (swarms) driven by an external...

2026-02-16-scs_inv_driver_rep_L63_full_driver_traj_with_burnin_driver_position_v0.20.0-138-g741985bc_traintest-narma10.tab

Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)

Tabular Data - 2.3 MB - 2 Variables, 102000 Observations -

DataTime Series

raw NARMA-10 time series file generated from the corresponding raw Lorenz-63 driver trajectory file (with burn-in phase)

2026-02-16-scs_inv_driver_rep_L63_full_driver_traj_with_burnin_driver_position_v0.20.0-138-g741985bc_traintest.tab

Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)

Tabular Data - 3.5 MB - 2 Variables, 102000 Observations -

DataTime Series

raw Lorenz-63 driver trajectory file (with burn-in phase) for computing the corresponding NARMA-10 time series file

agent-repulsion-driver-attraction-ensemble-100-seeds.tar.xz

Mar 11, 2026 - Replication Data for: Optimal information injection and transfer mechanisms for active matter reservoir computing (Gaimann and Klopotek, 2025)