Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
Smart Resilience: AI & XR in Sustainable Architecture explores how two powerful technologies are reshaping how we design buildings for a changing climate. You learn how AI supports data-driven, performance-based design — optimising energy, daylight, materials and resilience to climate stress — and how extended reality (VR and AR) brings designs to life for analysis, collaboration and stakeholder engagement. The course connects these tools to the goals of sustainable, resilient architecture: buildings that use less, adapt better and serve people well. You finish able to reason about applying AI and XR to a sustainable-design challenge. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI and extended reality (XR) in sustainable architecture — using data-driven design, simulation and immersive visualisation for resilient, low-impact buildings.
📋 Course Objectives
1. Apply AI to performance-based building design.
2. Optimise energy, daylight and materials with data.
3. Use VR and AR for design and collaboration.
4. Design for climate resilience and adaptation.
5. Connect tools to sustainable-architecture goals.
👥 Who Should Enroll?
• Architects and building designers
• Sustainability and building-performance professionals
• AI and XR technologists in the built environment
• Students of architecture and design technology
🚀 Key Learning Outcomes
• An understanding of AI and XR in architecture.
• A data-driven sustainable-design perspective.
• A foundation in smart building design.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Smart Resilience Foundations
Apply linear algebra and calculus principles to solve complex problems in AI and sustainable architecture • Develop a comprehensive understanding of machine learning fundamentals, including supervised and unsupervised learning techniques • Design and implement AI-powered systems that integrate with existing sustainable architecture frameworks
Data Engineering, Preprocessing, and Feature Pipelines
Configure and manage large datasets for AI model training, including data cleaning, preprocessing, and feature engineering • Analyze and visualize complex data structures to identify patterns and trends in sustainable architecture • Implement data pipelines that integrate with AI models to improve prediction accuracy and reduce errors
Model Architecture, Algorithm Design, and Smart Resilience Methods
Design and implement deep learning models, including convolutional neural networks and recurrent neural networks, for sustainable architecture applications • Evaluate and compare the performance of different AI algorithms, including decision trees, random forests, and support vector machines • Develop and deploy AI-powered models that integrate with existing sustainable architecture systems and frameworks
Training, Hyperparameter Optimization, and Evaluation
Train and optimize AI models using techniques such as grid search, random search, and Bayesian optimization • Analyze and evaluate the performance of AI models using metrics such as accuracy, precision, and recall • Implement techniques to prevent overfitting and improve the generalizability of AI models in sustainable architecture applications
Deployment, MLOps, and Production Workflows
Deploy AI models in production environments, including cloud-based and on-premises deployments • Design and implement MLOps workflows that integrate with existing DevOps pipelines and tools • Configure and manage AI model serving systems, including model monitoring, logging, and alerting
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze and identify potential biases in AI models and develop strategies to mitigate them • Develop and implement responsible AI practices, including transparency, explainability, and accountability • Evaluate and compare different techniques for ensuring fairness and equity in AI decision-making
Industry Integration, Business Applications, and Case Studies
Apply AI and XR technologies to real-world sustainable architecture problems and case studies • Develop and implement AI-powered solutions that integrate with existing industry workflows and systems • Evaluate and compare the business value and ROI of AI and XR investments in sustainable architecture
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Unity |
| Covered Tool / Platform | Unreal Engine |
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