AI-driven Adaptive Architecture for Climate Resilience
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Course Overview
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AI-driven Adaptive Architecture for Climate Resilience
Professional Certification Program
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FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
Program Syllabus
Module 1
About This Course
A three-day, hands-on workshop to build an AI-assisted workflow that unites microclimate data, simulation, and ML to predict heat/wind/flash-flood stress, auto-tune façades/ventilation, and deploy closed-loop building- and canyon-scale controls with decision-grade KPIs.
Module 2
Aim
Equip participants to build an end-to-end AI workflow that predicts heat/wind/flash-flood stress, auto-tunes façades/shading/ventilation, and deploys closed-loop controls and urban-canyon adaptations with decision-grade KPIs.
Module 3
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Application Details
This platform is an integrated learning and experimentation ecosystem for AI-driven heterogeneous catalysis. It combines structured modules, an AI sandbox for GNN modeling and EDA, real-time assessments, and an AI assistant (âCatalystBotâ), with built-in techno-economic and life cycle analysis to evaluate performance, cost, and sustainability of catalytic designs.
Module 4
Workshop Objectives
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Establish resilience KPIs & microclimate drivers.
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Assemble site data; run baseline E+/Honeybee/URBANopt/CFD; extract features.
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Train/validate fast ML surrogates for heat, wind, and flood.
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Optimize façade/shading/ventilation; auto-generate control schedules.
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Design rule-based/MPC control loops with sensing & fail-safes.
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Evaluate canyon-scale strategies; stress-test (heatwave + storm); deliver KPI dashboard & reproducible workflow.
Module 5
đ Day 1 â Fundamentals of COâ Capture, Catalysts & Data
- COâ Capture Routes & Products: post-/pre-combustion, DAC; RWGS, methanation, methanol
- Catalyst Families & Characterization: Ni/Co/Cu, bimetallics, perovskites; BET, XRD, XPS, TPD
- Data & Formats: OpenCatalyst/NIST, CSV/Parquet/JSON; units, metadata, reproducibility
- ThermoâKinetics Essentials: selectivity, activity, stability; mass/heat transfer basics
- ML Landscape: baselines (ridge/XGB), feature engineering (DFT descriptors), GNNs
- Hands-on: Build a tidy catalyst dataset; normalize units; run EDA and simple baselines
Module 6
đ Day 2 â ML for Catalyst Design, Screening & Uncertainty
- Representations: graphs for crystals/surfaces, SMILES, SOAP; Î-learning on top of DFT
- Modeling: message-passing GNNs, attention, transfer learning; multi-task property prediction
- Uncertainty & Calibration: ensembles, heteroscedastic heads, conformal prediction; CRPS
- Design of Experiments & BO: constraints (stability/cost), exploreâexploit trade-offs
- Hands-on: Train a compact GNN on adsorption energies (OC subset); evaluate Top-K hits
Module 7
đ Day 3 â Process Integration, TEA/LCA & Operationalization
- Reactor & Process Options: fixed-bed, slurry, microchannel; RWGS/methanation loops
- TEA & LCA: cost drivers, carbon intensity, system boundaries (ISO 14040/44), sensitivity
- Monitoring & Control: soft sensors, digital twins, MPC basics for selectivity/yield
- MLOps: data pipelines, validation, drift monitoring, model cards & governance
- Safety & Compliance: HAZOP overview, IEC 61511, GHG Protocol reporting snapshots
- Hands-on: One-page concept reportâflowsheet, KPIs, TEA/LCA snapshots, candidate shortlist
Module 8
Who Should Enrol?
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Architects, façade/building services engineers, and urban designers/planners
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Building performance/ESG/resilience leads and sustainability consultants
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CFD/modeling practitioners and data/ML engineers working in AEC
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Graduate students/researchers in architecture, civil/environmental engineering, and urban climate
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Municipal resilience/Smart City teams and risk/climate adaptation professionals
Module 9
Important Dates
Module 10
Registration Ends
11/26/2025
IST 7:00 PM
Module 11
Workshop Dates
11/26/2025 â 11/28/2025
IST 8 PM
Module 12
Workshop Outcomes
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ML predictors for heat, wind, and flash-flood stress
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Auto-tuned façade/shading/ventilation + control schedules
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Closed-loop controller (rule-based/MPC-ready) with fail-safes
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Urban-canyon adaptation plan with trade-off analysis
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KPI dashboard and a reproducible end-to-end workflow/templates
Module 13
Fee Structure
Module 14
Student
âš2999 | $110
Module 15
Ph.D. Scholar / Researcher
âš3999 | $130
Module 16
Academician / Faculty
âš4999 | $150
Module 17
Industry Professional
âš6999 | $200
Module 18
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Module 19
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 20
Publication Opportunity
Get published in a prestigious open-access journal.
Module 21
Centre of Excellence
Become part of an elite research community.
Module 22
Networking & Learning
Connect with global researchers and mentors.
Module 23
Global Recognition
Worth âš20,000 / $1,000 in academic value.
Module 24
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