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ML in Fluid Mechanics

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.
  • Keyward'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.
  • Keyward'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.
  • Keyward 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.

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