Build a Neural Surrogate for Computational Fluid Dynamics in HVAC Design
Overview
What this challenge is about.
Use a published CFD dataset (e.g., AirfRANS or a small in-house dataset if available) of around 1,000 steady-state airflow simulations on 2D building zones. Train a Fourier Neural Operator (FNO) or U-Net based surrogate that takes geometry and inlet velocity as inputs and outputs the velocity field. Hold out 100 cases for evaluation. Report mean field error and a designer-friendly metric (predicted vs. simulated dead-zone area). Wrap inference in a Jupyter notebook a building-services designer can drive without ML knowledge. Document the limits: when the surrogate is trustworthy and when CFD is still required.
The Brief
What you'll do, and what you'll demonstrate.
Train a neural-operator surrogate for steady-state HVAC airflow that gives a designer first-pass airflow predictions in seconds with documented accuracy bounds.
Earning criteria — what you'll demonstrate
- Train a neural operator (FNO or U-Net surrogate) for a PDE-governed field
- Translate a research-grade model into a designer-friendly inference interface
- Pick evaluation metrics that domain users (engineers) actually care about
- Quantify and communicate when a surrogate should and should not be trusted
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Applied AI Scientist
Connecting a research-grade neural operator to a measurable engineering workflow speedup is the day-one job of an applied AI scientist in an engineering consultancy.
This challenge sharpens
- neural-operators
- surrogate-modeling
- scientific-ml
Machine Learning Engineer
Packaging a model behind a designer-friendly notebook with clear trust boundaries is the MLE's productionization muscle.
This challenge sharpens
- pytorch
- model-evaluation
- surrogate-modeling
ML Researcher
Choosing the right neural-operator architecture for a PDE-governed field and reporting honest ablations is the researcher's craft.
This challenge sharpens
- neural-operators
- computational-fluid-dynamics
- pytorch