Hyperparameter optimization is the process of finding the best hyperparameters for a machine learning model. Hyperparameters are parameters that are set before training a model and can significantly affect the performance of the model.
Optuna is a Python framework for automated hyperparameter optimization.
simple.py: minimal script showing the objective/study/trial workflow.experiments.ipynb: methodology-first introduction to Optuna, including a controlled comparison of stochastic samplers and a machine-learning example with separate validation and test data.parameterized_heat_equation_optimization.ipynb: advanced case study using a parameterized heat-equation PINN. It compares random search with Optuna, confirms a shortlist across fresh training seeds, and reserves a final audit suite until after model selection.