Applied AI: Reduced Order Modelling and Explainability

This two-part webinar explores two key aspects of an AI-driven workflow. First, we discuss practical applications of advanced AI techniques to take high resolution data to create a reduced order model. There are both pros (speed) and cons (maybe reduced accuracy) in reduced order modelling, which we will explore. Second, a common question is that we now have this reduced order model (or any other advanced AI model) so how do I explore its workings and results?  Therefore, we will talk about explainable AI starting on the path to verification and validation with the ultime goal of producing certified systems with embedded AI for safety critical applications

Highlights

• Learn how to create a reduced order model
• Discuss the pros and cons of reduced order models
• See how start with explainable AI
• Explore the pathway to certified and verified AI

About the Presenter

Dr Peter Brady is a Principal Application Engineer with a background in numerical simulation, big data analysis and high-performance computing. Prior to joining MathWorks he worked at several civil and defence contractors undertaking detailed computational fluid dynamics investigations. At MathWorks Australia Peter supports the areas of: maths and statistics, machine and deep learning as well as providing an Australian based contact MathWorks’ autonomous customers to access global resources. He holds a bachelor’s degree in civil engineering and PhD in mechanical engineering.

Shine is a Senior Application Engineer at MathWorks with a background in machine learning and the Theory of Constraints (TOC). Over the past seven years, Shine worked as a Data Analyst at gold mining companies, contributing to a broad range of data-driven initiatives in both operational and technical domains. Shine holds an MPhil in Data Science, an MSc in Electrical and Computer Science, and a BSc in Biomedical Engineering.