Episode 7 | MACHINE LEARNING & OPTIMIZATION

Welcome to the seventh episode of the Gamma Technologies’ GT Tech Talk! In this episode, hosts Abhishek Jain, PhD and Divya Thiagarajan, PhD are joined by Ryan Dudgeon (Principal Application Engineer – Optimization, Machine Learning & Automation) and Yanni Papadimitriou (Greek Office Team Lead, Machine Learning and Optimization Development) to discuss machine learning and optimization!

Episode Timestamps

• 0:00 – Introduction to guests, Ryan Dudgeon and Yanni Papadimitriou
• 2:17 – Leveraging machine learning and optimization tools within GT-SUITE’s physics solvers
• 4:16 – Example: optimizing a thermal model
• 7:10 – Different productivity tools with GT-SUITE
• 11:40 – Various application use cases
• 16:37 – Machine learning capabilities and use cases
• 23:52 – Future of machine learning and optimization, including generative AI!
• 26:54 – Safran customer case study using machine learning capabilities
• 27:37 – Concluding thoughts

Mentions in the show

• Use of GT-SUITE at Safran Tech for the study of zero emissions aircrafts and propulsion systems (2023 European GT Technical Conference presentation)

Learn more:

Learn more about GT-SUITE’s machine learning and design of experiments capabilities

Watch the GT Tech Tip YouTube series on machine learning and optimization

Access our Design of Experiments (DOE) and Machine Learning training (note: you must have a GT-SUITE account to access this training)

Enhancing Model Accuracy by Replacing Lookup Maps with Machine Learning Models (Machine Learning Blog Part 1)

Optimizing Neural Networks for Modeling and Simulation (Machine Learning Blog Part 2)

Decreasing Battery System Simulation Runtime using Distributed Computing

Machine Learning Simulation – HVACR Industry

Sensitivity Analysis: How to Rank the Importance of Battery Model Parameters Using Simulation