From simulation powered design to predictive digital twins

▤ Summary

There will need to be a mechanism to label the failed and the successful attempts as such, but eventually, the algorithm will “learn” how to catch the ball. Initially, there will be many failures, but with many attempts, and with the appropriate labeling of the data (in this case probably the proud parents smiling and clapping when Junior makes a catch, and making commiserating sounds when she doesn’t), then the child will learn to catch the ball. But consider this – the machine learning algorithm has no clue about the laws of physics (and of course, neither does the child). It’s a fascinating proposition – and involves using conventional analytical tools to “inform” a machine learning model, thus dramatically reducing the amount of data required to train the ML algorithm. The application cited in the paper is the prediction of failure of an automotive component, but this approach has applications not only in manufacturing, but also in areas such as drug design, financial analysis and risk management, healthcare, and many more.

▦ Linked Companies

EASA Software

$25M to $50M