Applied AI for Reduced Order Modelling
Overview
This webinar focusses on the practical application of advanced AI techniques to accelerate modelling. It is common in technical work to have a highly detailed model, be that a dynamic model in Simulink, a computational fluid dynamics model of a turbulent flow or a thermal simulation using finite elements, but the penalty for this detail is in CPU and RAM compute requirements as well as long run times. Further, there is often a need to rapidly explore the parameter space or move to a different environment where you are willing to sacrifice some accuracy (how much is up to you) in a tradeoff for speed to answer. Here, we walk through a methodology to take these high-fidelity simulations and using advanced AI develop reduced order models for downstream applications. So, how do you know your reduced order model works? Come to the second webinar where we discuss techniques on the pathway to certified AI.
Highlights
- Learn the pros and cons of reduced order modelling
- Learn how to explore the parameter space of a model domain
- See how to take high fidelity simulations and apply AI to create a reduced order model
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.
Product Focus
This event is part of a series of related topics. View the full list of events in this series.
