Using Machine Learning (ML) for Computer-Aided Engineering (CAE) is an entirely different beast than using ML for other industries where we are used to seeing it the most, such as retail, recommendation engines, spam filtering, etc.

Unlike these industries, CAE does not have as much of an issue with confidentiality (knowing design parameter values does not help to reconstruct a design in most cases) and liability (engineers know the application physics and can verify the accuracy of the answer).  However, CAE suffers from a big challenge: not enough data or even no data! Unlike other industries where data flows in large volumes every second, in the CAE industry we are trained to survive with very minimal data. In this industry, data is expensive to obtain and in some cases needs to be created from scratch. You may find this hard to believe, but there are also many cases where the data is either not retained or not organized. Before rolling your eyes at such oversights, note that CAE data requires larger storage than most other types of data. Gigabyte sized results files for physics-based simulations is not uncommon. In addition, the disciplines under the CAE umbrella such as FEA, CFD, MBD are all performed by individual experts – in the absence of an organized central data management system, the data ends up residing on many different hard drives making it harder to be utilized in machine learning processes.

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SC18 — the world’s largest international conference for high-performance computing (HPC), networking, storage, and analysis — drew a mammoth crowd this November in Dallas, Texas. The event brought together scientists, engineers, software developers, policy makers, corporate managers, CIOs, IT administrators, and other professionals from all corners of the HPC and supercomputing industry. 12,000 attendees connected with 391 exhibitors at the Kay Bailey Hutchison Convention Center.

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