FUTURE MANUFACTURING
AI in simulation shortens development time
DR-ING . RAINER MÜMMLER
The capability and reach of artificial intelligence ( AI ) is continually expanding as the technology grows in complexity . As a result , engineers are faced with new challenges as they are tasked with integrating AI into systems . Part of the complexity stems from the recognition that AI models are only as effective as the data they ’ re trained with – if that data is insufficient , inaccurate , or biased , the model ’ s calculations will be , too . However , there are new methods in the field of simulation to overcome this problem .
At a high level , there are three ways AI and simulation are intersecting . The first has to do with addressing the challenge of insufficient data , as simulation models can be used to synthesize data that might be difficult or expensive to collect . The second is the use of AI models as approximations for complex highfidelity simulations that are computationally expensive , also referred to as reduced-order modelling . The third is the use of AI models in embedded systems for applications such as controls , signal processing , and embedded vision , where simulation has become a key part of the design process .
Data for Training and Validating AI Models
The process of collecting real-world data and creating good , clean , and catalogued data is difficult and time consuming . Engineers also must be mindful of the fact that while most AI models are static , they are constantly exposed to new data and that data might not necessarily be captured in the training set .
Simulation can help engineers overcome these challenges . In recent years , data-centric AI has brought the AI community ’ s focus to the importance of training data . Rather than spending all a project ’ s time tweaking the AI model ’ s architecture and parameters , it has been shown that time spent improving the training data can often yield larger improvements in accuracy .
With a model ’ s performance so dependent on the quality of the data it is being trained with , engineers can improve outcomes with an iterative process of simu-
Sources : MathWorks
Deep learning is used to create a reduced-order model of a physics-based engine model .
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