The beautiful intersection of simulation and AI

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Simulation has become an essential technology to help companies shorten time to market and reduce design costs. Engineers and researchers use simulation for a variety of applications, including:

Use a virtual model (also known as a digital twin) to simulate and test their complex systems early and often in the design process. Maintain a digital thread with traceability through requirements, system architecture, component design, code, and testing. Extend their systems to perform predictive maintenance (PdM) and failure analysis.

Many organizations are improving their simulation capabilities by integrating artificial intelligence (AI) into their model-based design. Historically, these two areas have been separated, but create significant value for engineers and researchers when used together effectively. The strengths and weaknesses of these technologies are perfectly aligned to help businesses solve three main challenges.

Challenge 1: Better training data for more accurate AI models through simulation

Simulation models can synthesize real-world data that is difficult or expensive to collect into quality, clean, cataloged data. While most AI models run using fixed parameter values, they are constantly exposed to new data that may not be captured in the training set. If left unnoticed, these models will either generate inaccurate information or fail outright, causing engineers to spend hours trying to figure out why the model isn't working.

Simulation can help engineers overcome these challenges. Rather than changing the architecture and parameters of the AI ​​model, it has been shown that time spent improving training data can often lead to greater improvements in accuracy.

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With the performance of a model so dependent on the quality of the data it is trained with, engineers can improve results through an iterative process of simulating data, updating a model, and AI, observing conditions it cannot predict well, and collecting other data simulations for those conditions.

Challenge 2: AI for new in-product features

Simulation has become an essential part of the design process for engineers using embedded systems for applications such as control systems and signal processing. In many cases, these engineers develop virtual sensors, devices that calculate a value that is not directly measured from available sensors. But the ability of these methods to capture the nonlinear behavior present in many real-world systems is limited, so engineers are turning to AI-based approaches that have the flexibility to model the complexities. They use data (measured or simulated) to train an AI model that can predict the unobserved state from the observed states and then integrate that AI model into the system.

In this case, the AI ​​model is part of the control algorithm that ends up on the physical hardware and usually needs to be programmed in a lower-level language, like C/C++. These requirements may place restrictions on the types of machine learning models appropriate for such applications, so technical professionals may need to try multiple models and compare accuracy and performance trade-offs on the device.

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