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by The digiLab Team

Updated 9 April 2024

digiLab Newsletter: Issue 6

Video and tutorial exploring the Predict capability in twinLab, and review of the workshop at the Henry Royce Institute
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Welcome to the latest newsletter. A shorter, interim one this fortnight - blame the Easter holidays!

Our Technical Content Lead, Ana, has produced an in-depth video and accompanying tutorial exploring the Predict capability in twinLab. We've also written up a review of our workshop at the Henry Royce Institute. Get in touch if your organisation would like a similar workshop on the use of Probabilistic ML for engineering workflows.

Using the Predict Capability in twinLab

This video and tutorial from Ana provides a detailed guide to the Predict capability in twinLab, which enables you to use a trained emulator to make predictions with built-in Uncertainty Quantification. This improves decision-making confidence and accelerates your exploration of the problem space.

Watch the Video

digiLab Co-Host ML Workshop at the Henry Royce Institute

We recently co-hosted an ML workshop at the prestigious Royce Discovery Centre, bringing together industry and academia to explore the intersection of machine learning (ML) with the domains of forging, advanced materials, and manufacturing.

The event drew representatives from key institutions including the Henry Royce Institute, Rolls Royce, the University of Strathclyde, the University of Manchester, WH Tildesley Ltd, and the University of Sheffield.

Read More

Getting Started with ML using twinLab

Just hit "Try twinLab" on our website, or reply to this email, and we'll set you up with an API key, the documentation, and some example solutions. Alternatively, if you've got a particular data challenge, book a free call with one of our solution engineers.

Best wishes,

Will and the digiLab Team

The digiLab Team
We are a 30+ strong team of ML Experts, Software Developers, Solution Engineers, and Product Experts. As a spinout from the University of Exeter, we build on years of cutting-edge academic research. At our core is a commitment to helping engineering and infrastructure companies become data-driven.

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