AutoML scripts for LSTM-Based DNNs with Optuna on HPC (alexwin9478/AutoMlforDnnTraining: Zenodo)
This repository contains Python code and documentation for an Automated Machine Learning (AutoML) workflow using the open-source framework Optuna ( https://optuna.org/ ). The AutoML studies focus on simultaneous Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS) for an LSTM-based Deep Neural Network (DNN) . The resulting models have been integrated into a Deep Nonlinear Model Predictive Control (NMPC) framework, as published in [Paper Link tbd]. Please see the ReadMe in the github Repo for more information.
Resource information
- Format
- zip
- Original repository
- 16903153
- DOI
- 10.5281/zenodo.16903153
- Credit basis
- Creators in Zenodo record metadata
- Access
- Open files available from the original Zenodo deposit
- Last checked
- Nov 5, 2026
- License
- apache2.0
- Last record update
- Nov 5, 2026
About this resource
This repository contains Python code and documentation for an Automated Machine Learning (AutoML) workflow using the open-source framework Optuna ( https://optuna.org/ ). The AutoML studies focus on simultaneous Hyperparameter Optimization (HPO) and Neural Architecture Search (NAS) for an LSTM-based Deep Neural Network (DNN) . The resulting models have been integrated into a Deep Nonlinear Model Predictive Control (NMPC) framework, as published in [Paper Link tbd]. Please see the ReadMe in the github Repo for more information.