‍Generative design is a design exploration process where engineers can input their design goals into the generative design software, along with parameters such as performance, materials, manufacturing methods, and cost constraints and the software then explores all the possible permutations of a solution and suggests a list of optimal design.

The quality of solutions suggested by generative design software has improved dramatically over the last years for structural analysis, but not in fluid or aerodynamic analysis. In this talk, we showed how deep learning tools borrowed from image processing can allow engineers to build their own generative designs solutions that also work for fluid dynamics problems.

Monolith enables engineers all over the world to:

• Understand physically intractable problems
• Fully explore multiple virtual test scenarios
• Reduce costs and time investment throughout the whole R&D cycle
• Increase confidence in predictions & recommendations on which tests to run next