Discover the cutting-edge applications of machine learning models in engineering use cases.
White paper summary:
Understand the application of Reduced Order Models in Engineering applications.
Explore Convolutional nerual networks (CNN) and its application to solve fluid mechanics problems with great accuracy.
Explore Graph neural networks (GNN) and their ability to capture complex relationships between entities.
Key Ward's Data Management Module (HUB) automates data extraction, organization, and preparation as an AI-digestion-ready dataset, enabling efficient collaboration and proper documentation of simulation data.
Key Ward's AI Prediction Module (FLOW) reduces development cycle time and costs, providing high-quality results and enabling multi-objective optimization of design spaces and flow evaluations.
Key Ward has strong experience in applying these various methods such as Convolutional Neural Networks (CNNs), Reduced Order Models (ROMs), Physics-Informed Neural Networks (PINNs), and Graph Neural Networks (GNNs) into various engineering applications.
