Enhancing Efficiency with Advanced Modelling and Simulation: Streamlining Your Engineering Workflow with Agentic AI
| Start Time | End Time |
|---|---|
| 11 Nov 2026, 8:00 PM EST | 11 Nov 2026, 9:00 PM EST |
Overview
How can you turn a production forecast into reliable plant performance, and move from an engineering idea to a tested solution more efficiently?
Join this webinar to see how MATLAB and Simulink connect production forecasting, operational insights, and control development in a model-based workflow. Learn how modelling and simulation help teams assess scenarios, manage uncertainty, and make informed decisions from planning through daily plant operations. We’ll also explore how soft sensors and digital twins guide operational decisions, while equipment health models support maintenance planning.
We’ll also explore how Agentic AI can support this engineering workflow, helping teams prototype and iterate faster while engineers review changes and validate outcomes.
Gain a practical understanding of how these approaches work together, the options for deploying your algorithms, and where to start applying them in your operation.
Highlights
- Connect production plans to business outcomes: Explore how models and simulation support production forecasting, optimisation, and evaluation of cost, energy, emissions and uncertainty.
- Turn plant data into operational insight: Understand how soft sensors, digital twins and equipment-health models can support operating and maintenance decisions.
- Move from analysis to operational use: Understand deployment options for enterprise analytics, operational technology and production controllers, while retaining engineering knowledge.
- Explore agentic AI as an engineering assistant: Identify opportunities to accelerate coding, modelling, simulation and testing, with engineers reviewing decisions and validating results.
About the Presenter
Shine Rezaei
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.
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