Bridging the Gap Between Machine Learning and CAE
We all know that CAD is for designing a product and CAE is for testing and simulating it.
Computer-Aided Engineering (CAE) is a tool that supports finding the outcome by applying a discrete solution of partial differential equations for the phenomena to be analyzed.
CAE reduces the potential for errors in design, users avoid over-engineering, and the effect of altering a few parameters on the product can be studied.
ML systems improve the performance of an existing system based on their learning from past experience.
Consider a design system that proposes the dimensions of a car bumper beam using a set of equations. The program outputs the beam dimensions using input data that is provided by the designer. If, for example, the span-to-depth ratio is not satisfactory from an aesthetic viewpoint, the designer must manually introduce modifications. Most conventional programs provide no mechanism for the treatment of feedback. Therefore the design program continues to suggest the same dimensions for the same input, no matter how many times the designer rejects the design.
What if the CAE software asked the user for feedback on the generated simulation? The code generalizes and adapts to the identified weaknesses and by incorporating this experience in its Machine Learning core, the system can then avoid similar weak output in the future. In turn, improving performance.
This is one simple way of how ML algorithms can learn through their weaknesses to provide stronger better CAE Simulations later.