Event details
Create testing plans more efficiently in automotive and aerospace product development
Next Test Recommender (NTR), created by CoreWeave, enables users to train and assess machine learning models, offering valuable recommendations for optimal test conditions to apply in the next round of testing. NTR assesses previously gathered data to suggest the most effective new tests to conduct.
In this on-demand webinar, Dr. Joel Henry, Dr. Gareth Jones, and Jousef Murad discuss how CoreWeave Physical AI software enhances test engineers’ capabilities to iteratively optimize their test campaigns by maximizing the value derived from the time allocated to a test campaign or by reducing the time taken to achieve a certain quality of testing.
This knowledge and human-in-the-loop design empower engineers to make informed decisions, optimize their test plans, and maintain responsibility for continuously improving the final product’s safety, quality, and reliability.
Learning objectives:
- Learn about the different engineering test plan strategies, their benefits, and their limitations (random vs. uncertainty vs. robust active learning, etc.)
- Predict a new test’s outcome ahead of time
- Leverage your test data to train and evaluate AI models to make efficient use of testing times in expensive test facilities
- Understand the impact of test conditions and understand which test conditions are most important to vary from one test to the next
- Use interactive prediction features to identify the next tests to run
