1. Running with a .yaml config file will lead to usual execution.
2. Running with the --optimized flag will tune hyperparameters with optuna. No input file needed.

1. objective.py defines the objective function for tuning. Contains the fastvpinns object returning metric for tuning.
2. optuna_tuner.py manages the hyperparameter tuning process.

…rgument.

1. Accept number of trials and number of training iteration for each trial as an argument.

1. Accept an is_optimized argument, True if hyperparameter optimization with Optuna is being used.
2. If is_optimized is True, geometry module doesn't print out the test mesh and VTK file for each trial.
3. Backward compatibility - default value of is_optimized is False, existing code with config file should work as is.

1. Creates an SQLite database if it doesn't exist. Can be used for stalled runs or parallel implementation.
2. Lists available number of GPUs and divides jobs.