The Challenge

In a manufacturing setting, designing and building a new product such as a car engine or a wind turbine typically takes a long time and can be quite costly.

As the volume and complexity of data increases, it is extremely challenging for engineers – even those with access to relatively sophisticated computing and analytical resources – to make sense of all the multi-dimensional information and make sound decisions in a timely manner. This uncertainty increases the number of costly experimental test campaigns, lengthens development timescales, and raises the cost of development.

What Argonne Delivers

The traditional approach to design optimization of a new product involves a lot of experimental testing, evaluating prototypes, and going through multiple design iterations until you come up with a set of promising designs. 

In an effort to combat these limitations, industry increasingly relies on high-fidelity computer models as virtual representations of real-world devices. High-fidelity modeling represents an improvement over costly physical development and testing, but it remains time-consuming.

Argonne’s solution is to augment the high-fidelity modeling with machine learning to dramatically accelerate the process while maintaining the reliability of the data. Argonne’s rare combination of world-class computing resources and inter-disciplinary research teams offers its collaborators a leg up on the competition.

The Results

By tapping into Argonne’s cutting-edge machine learning techniques, organizations reduce design time from months to days, translating to millions of dollars in savings.

Applicable Industries

• Manufacturing
• Transportation
• Energy
• Materials
• Construction
• Utilities