ML IN CAE
Bridging the Gap Between Machine Learning and CAE
Importance of Machine Learning in Computer-Aided Engineering
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.
The need for Machine Learning for future of CAE in the product development process and PEA
Even though CAE has built a strong reputation as a verification, troubleshooting and analysis tool, there is still a perception that sufficiently accurate results come rather late in the design cycle to really drive the design. Smart systems lead to an increased need for multi-physics analysis including controls and contain new materials, to which engineers are often less familiar.
By integrating Machine Learning we facilitate powerful problem solving, by ensuring better use of computer resources and including ML based optimizations…
