This guest contribution on the Altair Blog is written by Nicholas Gaul, Senior Application Engineer at RAMDO Solutions, a part of the Altair Partner Alliance.

The Department of Defense needs reliable mobility maps for efficient mission planning of troop movement. These maps are important for decision makers to plan routes and operations, select capable vehicles, and assess mission success or failure. The stakes are high, as mission failure often means potential loss of life. RAMDO software, when used in conjunction with terramechanics simulations allows the engineer a way to account for terrain variability, providing improved mobility maps.

The Department of Defense knew their current method of predicting the speed at which they could travel would not be accurate without addressing the variability in the terrain data. One of the biggest reasons traditional mapping is not reliable is because it does not account for the variability of terrain such as slope, elevation, soil properties, etc. Sometimes their data would indicate a route was possible, when in fact, they would get stuck. And on the flip side, it might show a route as impenetrable, when actually, it was a viable option.

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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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