Unlocking AI/ML Potential for Engineering Designs and Manufacturing Simulations

You can watch the recorded seminar from this link.

Presenter: Jaideep Bangal – Director of Simulation & Design

Abstract: As digital engineering advances, modeling, and simulation will converge with machine learning, AI, and high-performance computing (HPC) in solving the world’s most complex problems. PhysicsAI is a new tool designed to predict physics outcomes quickly. This technology leverages historical simulation data to deliver fully animated physics predictions in a fraction of the time it takes traditional solvers to do the same. Unlike previous machine learning technologies, PhysicsAI uses cutting-edge geometric deep learning to operate directly on meshes and CAD models, which generates even faster results.

During this presentation, we discuss:

• How AI is used in computational engineering, with focus on manufacturing feasibility simulations and predictions using PhysicsAI.
• The challenges present in AI adoption, such as the difficulty of collecting meaningful data for training machine learning algorithms and capturing and understanding design intent.
• Specific AI tools and technologies that can be used in computational engineering, such as
  • Altair® romAI™ for generating reduced-order models (ROMs),
  • Altair® PhysicsAI™ for quick physics predictions, and
  • DesignAI for combining physics-based, simulation-driven design with machine learning-based AI-driven design.