Feature/data generation automl

[YGMaerz]

Merge branch 'feature/data-generation-automl' into main

Introduce Data Generation and Transformation Components for Darcy AutoML Active Learning

This merge integrates the comprehensive data generation and transformation functionalities developed in the
'feature/data-generation-automl' branch into the main branch. The key contributions include:

• Data Descriptors and Data Generator: Established structured data descriptors and a generator
  to create datasets tailored for various machine learning models within the Darcy AutoML active learning
  framework.

• OntologyEngine: Developed an engine to analyze and determine the necessary data transformations
  based on model-specific requirements, ensuring data compatibility and consistency across different stages.

• OntologyTransformationEngine: Implemented a transformation engine that executes the
  suggested data transformations, handling tasks such as copying files, managing subfolders, and
  preparing data for preprocessing and feature preparation stages.

• Notebook Cells for Data Generation and Processing: Created Jupyter notebook cells that
  facilitate the generation of transformation plans and the subsequent processing of data through
  various pipeline stages, enhancing reproducibility and ease of experimentation.

• ModelRegistry with Model Descriptors: Established a registry to manage and maintain model descriptors
  for different machine learning models (e.g., FNO, AFNO), enabling seamless integration and selection
  of models within the AutoML pipeline.

These enhancements collectively streamline the data preprocessing workflow, support multiple model types,
and lay the foundation for scalable and efficient active learning processes in the Darcy AutoML project.