Simulation has emerged as a critical technology for helping businesses shorten time-to-market and lowering design costs. Engineers and researchers use simulation for a variety of applications.
Copyright: venturebeat.com – “The beautiful intersection of simulation and AI”
Applications using simulation:
- Using a virtual model (also known as a digital twin) to simulate and test their complex systems early and often in the design process.
- Maintaining a digital thread with traceability through requirements, system architecture, component design, code and tests.
- Extending their systems to perform predictive maintenance (PdM) and fault analysis.
Many organizations are improving their simulation capabilities by incorporating artificial intelligence (AI) into their model-based design. Historically, these two fields have been separate, but create significant value for engineers and researchers when used together effectively. These technologies’ strengths and weaknesses are perfectly aligned to help businesses solve three primary challenges.
Challenge 1: Better training data for more accurate AI models with simulation
Simulation models can synthesize real-world data that is difficult or expensive to collect into good, clean and 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 unnoticed, these models will generate inaccurate insights or fail outright, causing engineers to spend hours trying to determine why the model is not working.
Simulation can help engineers overcome these challenges. Rather than tweaking the AI model’s architecture and parameters, it has been shown that time spent improving the training data can often yield more extensive 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 simulating data, updating an AI model, observing what conditions it cannot predict well, and collecting more simulated data for those conditions.
Der Beitrag The beautiful intersection of simulation and AI erschien zuerst auf SwissCognitive, World-Leading AI Network.