How AI-driven modelling can change the way products are tested.

‍Engineers aim to use the best technology available to get the most from their data; with the ultimate goal being to understand physical relationships, increase product performance, while simultaneously reducing test times.

Monolith provides a new AI solution enabling engineers to build self-learning, intelligent models to reduce the amount of testing typically needed to build and validate quality products faster. ‍Surrogate models are an essential engineering tool. Their popularity has recently increased due to the high cost of evaluating real-world simulations alongside real-world testing.

This webinar explains how engineers can use Monolith to quickly understand and instantly predict performances or new composites, while reducing testing time. Engineers are able to use a mixed-categorical data-driven approach for predictions and optimizations of hybrid discontinuous composites performance. Additionally, Monolith allows engineers to easily: predict results, understand relationships, understand sensitivities between input parameters and their component/product performance, and leverage their past test experience and datasets.

Our speakers Raul Carreira Rufato, PhD Student, and Monolith's Principal Engineer Joël Henry, show how self-learning models can be leveraged to work with mixed engineering data types, and how this approach can be used to improve product life cycles as well as test routines & predictions.