Maintaining oil and gas machinery is expensive—but predictive maintenance models can help engineers minimize repairs and downtime.

Shayan Mortazavi and Alex Lowden, Data Scientists at Accenture in the Industrial Analytics Group, work on the development of predictive maintenance models to minimize downtime of systems. In this episode, they discuss the complications when building these models, such as limited access to failure data and the massive number of features available, as well as the need for explainability and interpretability in their models. They also share how SigOpt’s parallelism feature allowed them to accelerate model development.

• 1:27 - Intros
• 3:05 - Machinery maintenance, then vs now
• 4:06 - Goals of maintenance
• 6:49 - Challenges of predictive maintenance for oil and gas
• 8:31 - Human in the loop element
• 10:07 - Interpretability
• 11:42 - Using SigOpt to optimize hyperparameters
• 13:50 - Managing multiple LSTMs
• 16:38 - Using SigOpt's multimetric optimization
• 18:36 - Predicting ultimate machine failure
• 20:39 - Getting teams on board with AI-based tools
• 23:21 - Overconfidence of AI 

Learn more about SigOpt at sigopt.com and follow us on Twitter at twitter.com/sigopt

Learn more about Accenture: https://www.accenture.com

Subscribe to our YouTube channel to watch Experiment Exchange interviews: https://www.youtube.com/channel/sigopt

How do you design a better glass?

Paul Leu, Associate Professor of Industrial Engineering at the University of Pittsburgh, shares the insights behind his interdisciplinary work using machine learning to design and test novel glass structures. He and Michael McCourt discuss the collaboration between Pitt and SigOpt, the challenges of glass design and testing, and what's ahead for the Year of Glass.

• 0:23 - Intros
• 5:14 - Paul's interdisciplinary philosophy
• 7:04 - Nanomaterials research
• 11:48 - Designing for the properties of light
• 14:35 - How Paul and his team use SigOpt
• 17:50 - Designing better solar panels
• 21:05 - How Paul uses SigOpt's advanced features
• 22:51 - The Year of Glass
• 24:16 - Paul's work with MDS-Rely 

Learn more about SigOpt at sigopt.com and follow us on Twitter at twitter.com/sigopt

Learn more about LAMP: https://lamp.pitt.edu/ 

Learn more about MDS-Rely: https://mds-rely.org/ 

Learn more about the Year of Glass: https://www.iyog2022.org/ 

Subscribe to our YouTube channel to watch Experiment Exchange interviews: https://www.youtube.com/channel/sigopt

Given a property, what’s the material or the molecule that achieves it?

This is the question behind some of Rafael Gomez-Bombarelli's latest work. Tune in as SigOpt's Michael McCourt interviews the Assistant Professor of Materials Processing at MIT about his development of machine learning strategies to design new materials—including fluids, cloths, metals, and nanomaterials. 

• 1:28 - Intros
• 2:18 – Using ML to predict physical properties of molecules
• 3:48 – Rafael's active learning process
• 5:23 - ML costs
• 6:34 – How Rafael uses SigOpt in his work
• 7:59 - Modeling the color change properties of molecules
• 9:26 - Multi-fidelity methods
• 12:12 – Continuum simulations
• 14:57 – Inverse design problems
• 18:50 - Workshops at ML conferences
• 20:47 – What's next for Rafael

Learn more about SigOpt at sigopt.com and follow us on Twitter at twitter.com/sigopt 

Subscribe to our YouTube channel to watch Experiment Exchange interviews: https://www.youtube.com/channel/sigopt