About Workshop
The workshop is a three-day hands-on program designed to equip participants with practical skills in creating and leveraging digital twins for sustainable infrastructure. Day 1 focuses on foundational concepts, including digital twin architecture, sustainability and climate-risk indicators, and developing frameworks for buildings, campuses, and industrial systems, complemented by hands-on exercises in system mapping and indicator matrix preparation using Google Colab, Python, Google Sheets, and QGIS. Day 2 introduces AI-assisted simulation for climate-impact analysis, teaching participants to model resource flows, energy demand, and operational stress scenarios, with practical exercises in SimPy/AnyLogic and Python for scenario modeling and pattern detection. Day 3 emphasizes decision intelligence, guiding participants in converting digital twin and simulation outputs into actionable dashboards, visualizing climate risks, comparing scenarios, and supporting ESG reporting and infrastructure planning, with hands-on projects in Power BI, QGIS, and Python to develop final climate-resilience and sustainability decision dashboards.
Aim
This program is designed to enable participants to develop hands-on practical skills in creating digital twins for climate-resilient and sustainable systems. Through immersive learning, participants will explore AI-assisted simulation techniques, perform climate-impact analyses, and design comprehensive sustainability dashboards. The course empowers learners to leverage data-driven insights to optimize decision-making for buildings, campuses, industrial operations, and urban infrastructure, ensuring that solutions are not only technologically robust but also environmentally responsible and adaptive to future climate challenges.
What Participants Will Learn
- Introduce digital twins for sustainable infrastructure and climate-resilient planning.
- Explain AI applications in monitoring, prediction, and operational decision-making.
- Teach mapping of physical systems to virtual models with data and decision layers.
- Guide identification of sustainability and climate-risk indicators such as energy use, carbon impact, water efficiency, and flooding risk.
- Enable simulation-based scenario modeling for climate impacts and system performance.
- Develop dashboards for actionable insights, ESG reporting, and resilience planning.
Structure
Day 1: Foundations of Digital Twins and Climate-Resilient Sustainability
- Introduction to digital twins for sustainable buildings, campuses, and industrial systems
- Understanding the role of AI in climate-resilient infrastructure planning
- Concept of physical system, virtual model, data layer, and decision layer
- Key sustainability indicators: energy use, carbon impact, water use, resource efficiency, and operational performance
- Climate-risk indicators: heat stress, flooding risk, air quality, extreme weather, and infrastructure disruption
- How digital twins support monitoring, simulation, prediction, and decision-making
- Developing a digital twin framework for a building, campus, or industrial facility
🛠️ Hands-on:
- Hands-on 1: Digital Twin System Mapping for a Building / Campus / Industrial Facility
- Hands-on 2: Climate-Risk and Sustainability Indicator Matrix Preparation
📅 Day 2: AI and Simulation for Climate Impact Analysis
- Introduction to simulation-based sustainability planning
- Understanding system behavior through resource flows, occupancy, energy demand, and operational load
- Basics of discrete-event simulation using SimPy / AnyLogic
- Using Python for climate and sustainability data analysis
- Scenario modeling for heatwaves, energy demand, flooding disruption, or operational stress
- AI-assisted pattern detection for climate risk and system performance
- Interpreting simulation results for sustainability and resilience decisions
🛠️ Hands-on:
- Hands-on 1: Basic Climate-Impact Simulation Using SimPy / AnyLogic
- Hands-on 2: AI-Assisted Sustainability Scenario Analysis in Python
📅 Day 3: Climate-Resilience Dashboards and Decision Intelligence
- Converting digital twin and simulation outputs into decision-ready insights
- Designing dashboard indicators: energy demand, carbon impact, resilience score, climate risk, and operational efficiency
- Using QGIS for spatial climate-risk and sustainability visualization
- Scenario comparison: baseline system vs climate-resilient intervention
- Creating decision dashboards for buildings, campuses, industrial systems, and urban facilities
- Using dashboards for ESG reporting, infrastructure planning, facility management, and sustainability communication
- Developing a final digital twin concept model for research or professional application
🛠️ Hands-on:
- Hands-on 1: Climate-Resilience Dashboard Creation in Power BI
- Hands-on 2: Mini Project: Digital Twin-Based Climate Scenario and Sustainability Decision Dashboard.
Tools Covered: Python, Google Colab, Google Sheets, QGIS, Power BI, SimPy, AnyLogic
Important Dates
Registration Ends
4.30 pm
Workshop Dates
2026-06-11
5.30 PM
5.30 PM
What You Will Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Outcomes
- Understand the fundamentals of digital twins and their role in climate-resilient sustainable systems.
- Map physical systems to virtual models and identify key sustainability and climate-risk indicators.
- Perform AI-assisted climate-impact analysis and scenario-based simulation using Python, SimPy, or AnyLogic.
- Develop dashboards in Power BI and QGIS for sustainability monitoring and decision-making.
- Compare baseline and intervention scenarios to inform ESG reporting and infrastructure planning.
- Apply hands-on digital twin techniques to real-world systems for actionable insights and resilience planning.
Who Should Attend
- Industry professionals, sustainability analysts, urban planners, and facility managers.
- PhD scholars, researchers, and postgraduate students in environmental science, civil/industrial engineering, or climate studies.
- Data scientists, AI practitioners, and engineers interested in digital twins, simulation, and resilience planning.
- Professionals and students seeking hands-on experience with AI-assisted digital twin frameworks and sustainability dashboards.
- No prior advanced AI or simulation expertise required; basic familiarity with Python or Excel is helpful.
Deliverables
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience