The popularity of the design, build, and test approach to engineering is fast-waning as today’s engineers face unprecedented pressure to innovate, keep pace with the latest technologies, and design creative solutions to urgent problems. 

Consider, for example, automated driving systems. Although autonomous vehicles promise to significantly improve mobility, engineers must test these frameworks for critical factors such as safety and potential system failures. Toyota is one of the automakers working to make driverless systems safe. In 2016, Toyota president and CEO Akio Toyoda said more testing would be needed to complete its mission—some 8.8 billion miles of it. 

Fortunately, says Stefan Jockusch, vice president of strategy at Siemens Digital Industries Software, simulation can help. By virtually testing millions of real-world scenarios, from snowy road conditions to careless pedestrians, simulation technology can analyze autonomous vehicles’ performance while accelerating development and reducing costs. 

But while simulation is critical to the digital development and manufacturing of today’s and tomorrow’s products, challenges such as increased complexity and a lack of domain knowledge are prompting organizations to bolster their simulation processes with artificial intelligence (AI) capabilities. 

I as intelligent augmenter 

Although challenges can vary, Don Tolle, a director at consulting and research company CIMdata, says, “one of the key barriers to simulation is the fair amount of time it takes to turn around a complex simulation and share the results with others, including design engineers and simulation analysts.” In fact, Tolle says it can take “weeks” to design, collect information, build, execute, and analyze simulation models to support decision-making. 

Complexity is another obstacle engineers must tackle. Simulation models can provide deeper and more accurate insights into the behavior of manufacturing systems—but these additional details can come at the cost of greater computation. Building simulation models also demands talent with deep domain and mathematical knowledge. Many

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By: Martha Leibs
Title: Intelligent models for smarter decision-making
Sourced From:
Published Date: Thu, 18 Feb 2021 16:05:00 +0000

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