Accurate & Efficient Self-Learning Models for Composite Materials in Engineering R&D
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.
Who should watch?
- Examples & demos for AI in material testing
- Provide guidelines to engineers to identify use cases
- How Machine Learning (ML) can complement simulations and accelerate tests & measurements
Who should attend? Engineers who …
- Want to perform fewer physical tests and learn more from their engineering test data
- Are currently limited by traditional desktop ML and lack a data science background
- Professionals who want to understand the benefits of cloud-based AI
- Everyone who is interested in finding out how to get easy access to an all-in-one AI solution
Monolith enables engineers all over the world to:
- Understand physically intractable problems
- Fully explore multiple virtual test scenarios
- Reduce costs and time investment throughout the whole R&D cycle
- Increase confidence in predictions & recommendations on which tests to run next
Hear from our speakers
Raul Carreira Rufato
Raul Carreira Rufato has a double degree in Aeronautical and Aerospace Engeneering from ISAE-SUPAERO (France) and UFU (Brazil), specialized in signals and systems, electronics, flight control and machine learning. He has participated in several research projects developing solutions for major companies such as: Safran and Monolith AI. He is currently doing a PhD in Sorbonne Université and Safran Tech.
Joel is our Principal Engineer and has worked with the majority of our clients to create lasting value for them. He has won the Imperial College Award for the best PhD thesis and has previously worked on improving test and simulation methods in the aerospace industry.
Jousef is responsible for product marketing at Monolith. He studied mechanical engineering at the Karlsruhe Institute of Technology (KIT) where he focused on computational mechanics, turbulence modeling & AI.