Home >Courses >AI-driven Adaptive Architecture for Climate Resilience

NSTC Logo
Home >Courses >AI-driven Adaptive Architecture for Climate Resilience

11/26/2025

Registration closes 11/26/2025
Mentor Based

AI-driven Adaptive Architecture for Climate Resilience

urning weather into signals—and buildings into responses.

  • Mode: Virtual / Online
  • Type: Mentor Based
  • Level: Advanced
  • Duration: 3 Days
  • Starts: 26 November 2025
  • Time: 8 PM IST

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.

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.

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 (“CatalystBot”), with built-in techno-economic and life cycle analysis to evaluate performance, cost, and sustainability of catalytic designs.

6e1488c1 capture 4

Workshop Objectives

  • Establish resilience KPIs & microclimate drivers.

  • Assemble site data; run baseline E+/Honeybee/URBANopt/CFD; extract features.

  • Train/validate fast ML surrogates for heat, wind, and flood.

  • Optimize façade/shading/ventilation; auto-generate control schedules.

  • Design rule-based/MPC control loops with sensing & fail-safes.

  • Evaluate canyon-scale strategies; stress-test (heatwave + storm); deliver KPI dashboard & reproducible workflow.

Workshop Structure

📅 Day 1 – Fundamentals of CO₂ Capture, Catalysts & Data

  • CO₂ 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
  • Thermo–Kinetics 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

📅 Day 2 – ML for Catalyst Design, Screening & Uncertainty

  • Representations: graphs for crystals/surfaces, SMILES, SOAP; Δ-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), explore–exploit trade-offs
  • Hands-on: Train a compact GNN on adsorption energies (OC subset); evaluate Top-K hits

📅 Day 3 – 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 report—flowsheet, KPIs, TEA/LCA snapshots, candidate shortlist

Who Should Enrol?

  • Architects, façade/building services engineers, and urban designers/planners

  • Building performance/ESG/resilience leads and sustainability consultants

  • CFD/modeling practitioners and data/ML engineers working in AEC

  • Graduate students/researchers in architecture, civil/environmental engineering, and urban climate

  • Municipal resilience/Smart City teams and risk/climate adaptation professionals

Important Dates

Registration Ends

11/26/2025
IST 7:00 PM

Workshop Dates

11/26/2025 – 11/28/2025
IST 8 PM

Workshop Outcomes

  • ML predictors for heat, wind, and flash-flood stress

  • Auto-tuned façade/shading/ventilation + control schedules

  • Closed-loop controller (rule-based/MPC-ready) with fail-safes

  • Urban-canyon adaptation plan with trade-off analysis

  • KPI dashboard and a reproducible end-to-end workflow/templates

Fee Structure

Student

₹2999 | $110

Ph.D. Scholar / Researcher

₹3999 | $130

Academician / Faculty

₹4999 | $150

Industry Professional

₹6999 | $200

What You’ll Gain

  • Live & recorded sessions
  • e-Certificate upon completion
  • Post-workshop query support
  • Hands-on learning experience

Join Our Hall of Fame!

Take your research to the next level with NanoSchool.

Publication Opportunity

Get published in a prestigious open-access journal.

Centre of Excellence

Become part of an elite research community.

Networking & Learning

Connect with global researchers and mentors.

Global Recognition

Worth ₹20,000 / $1,000 in academic value.

Need Help?

We’re here for you!


(+91) 120-4781-217

★★★★★
Prediction of Protein Structure Using AlphaFold: An Artificial Intelligence (AI) Program

Thanks for the very attractive topics and excellent lectures. I think it would be better to include more application examples/software.

Yujia Wu
★★★★★
AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp

really badly prepared, expected much more of this especially when basic programming knowledge is being required by participants it would be nice to learn something additional and actually discuss the topics that were announced

Franziska Singer
★★★★★
Scientific Paper Writing: Tools and AI for Efficient and Effective Research Communication

Excellent delivery of course material. Although, we would have benefited from more time to practice with the plethora of presented resources.

Kevin Muwonge
★★★★★
AI-Powered Multi-Omics Data Integration for Biomarker Discovery

1. You were reading from the slides. You were not teaching
2. You did not teach concepts. You were just repeating obvious ideas about integrative biology.
3. You were not paying attention to the audience. They were raising hands and writing on chat.
4. Too much content. Critical and necessary ideas were not explained.

Abhijit Sanyal

View All Feedbacks →

Stay Updated


Join our mailing list for exclusive offers and course announcements

Ai Subscriber