Add to ORKGSimulation data, python code and figures used in the publicationKeup, Kühn, Dahmen, Helias (2021) Transient chaotic dimensionality expansion by recurrent networks. Physical Review X 11, 021064, doi: 10.1103/PhysRevX.11.021064.Please see README.md for a detailed description and execution instructions
This data set corresponds to the python scripts and data that were used for the paper : “Approximate...
This is the Python code for the Book Chapter Title "Analysis of Long Short Term Memory (LSTM) Networ...
Version 2 changes: - Added a description and an instruction for random_seed_changer function and rem...
Simulation data, python code and figures used in the publication Keup, Kühn, Dahmen, Helias (2021) ...
Numerical simulation code and data for the paper: Reconciling two notions of quantum operator disag...
The provided code allows the generation and application of machine learning surrogate models based o...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
This is a reproducibility package for "Higher-Order Patterns Reveal Causal Timescales of Complex Sys...
This includes the data generated by simulations in the work titled "Growth Rules for the Repair of A...
This is part three of the Nonlinear Science Webinar, titled "Data-driven dimension reduction, dynami...
Here, we include the data and code needed to reproduce the figures in "Programmable large-scale simu...
Acknowledgments This work was supported by ONR under Grant No. N00014-21-1-2323. Data availability s...
Supplementary data and R/Python code required to reproduce the figures from the accompanying publica...
This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Speci...
This material relates to the article 'Chaos in a bacterial stress response' by Choudhary et al. The ...
This data set corresponds to the python scripts and data that were used for the paper : “Approximate...
This is the Python code for the Book Chapter Title "Analysis of Long Short Term Memory (LSTM) Networ...
Version 2 changes: - Added a description and an instruction for random_seed_changer function and rem...
Simulation data, python code and figures used in the publication Keup, Kühn, Dahmen, Helias (2021) ...
Numerical simulation code and data for the paper: Reconciling two notions of quantum operator disag...
The provided code allows the generation and application of machine learning surrogate models based o...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
This is a reproducibility package for "Higher-Order Patterns Reveal Causal Timescales of Complex Sys...
This includes the data generated by simulations in the work titled "Growth Rules for the Repair of A...
This is part three of the Nonlinear Science Webinar, titled "Data-driven dimension reduction, dynami...
Here, we include the data and code needed to reproduce the figures in "Programmable large-scale simu...
Acknowledgments This work was supported by ONR under Grant No. N00014-21-1-2323. Data availability s...
Supplementary data and R/Python code required to reproduce the figures from the accompanying publica...
This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Speci...
This material relates to the article 'Chaos in a bacterial stress response' by Choudhary et al. The ...
This data set corresponds to the python scripts and data that were used for the paper : “Approximate...
This is the Python code for the Book Chapter Title "Analysis of Long Short Term Memory (LSTM) Networ...
Version 2 changes: - Added a description and an instruction for random_seed_changer function and rem...