
Digital Twins for Climate-Resilient Sustainable Systems
Build, Simulate, and Monitor Climate-Resilient Systems Using AI-Powered Digital Twins.
Skills you will gain:
About Program:
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.
Program Objectives:
- 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.
What you will learn?
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
Mentor Profile
Fee Plan
Get an e-Certificate of Participation!

Intended For :
- 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.
Career Supporting Skills
Program 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.
