New Year Offer End Date: 30th April 2024
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Program

AI-driven Adaptive Architecture for Climate Resilience

urning weather into signals—and buildings into responses.

Skills you will gain:

About Program:

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.

Program 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.

What you will learn?

📅 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

Mentor Profile

Fee Plan

INR 1999 /- OR USD 50

Get an e-Certificate of Participation!

2024Certfiacte

Intended For :

  • 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

Career Supporting Skills

Program 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