AI-Enabled Digital Twins

About this course

Apply AI within an operational digital twin. Explore state estimation, anomaly detection, demand forecasting, model calibration and optimisation, with uncertainty, validation and ongoing monitoring built into the workflow.

This advanced course is part of the Smart Cities, Digital Twins & Urban Systems school. Delivery: Instructor-Led / Cohort. Estimated study time: 22 h.

Course curriculum

• Module 1 — AI Inside a Twin
• Module 2 — State Estimation and Data Assimilation
• Module 3 — Anomaly Detection on Networks
• Module 4 — Forecasting Demand with Uncertainty
• Module 5 — Calibrating Simulation Models
• Module 6 — Surrogate Models
• Module 7 — What-If and Optimisation
• Module 8 — Validating and Monitoring a Predictive Twin

Applied learning

• Case Study — The Burst Alarm That Cried Wolf
• Lab 1 — Detect and Locate Bursts in Castle Rise
• Lab 2 — A Calibrated Hydraulic Surrogate for Fast Scenarios
• Lab 3 — A Demand Forecast with Intervals and a Backtest
• Capstone — An AI Component for an Operational Twin

Who this course is for

Engineer / Architect; Government Professional

Prerequisites

NE-DT-202

Course details

• Course code: NE-DT-301
• Level: Advanced
• Estimated duration: 22 h
• Delivery: Instructor-Led / Cohort

Includes online course lessons and practice activities. Course scenarios use simulated data. Course materials are in pilot review.