README.md

metadata

license: apache-2.0
task_categories:
  - image-to-image
tags:
  - physics
  - cfd
  - digital-twin
  - datacenter
  - surrogate-model
  - neural-operator
  - nvidia
  - physicsnemo
pretty_name: 'Boreas: Datacenter Digital Twin Surrogate Predictions'
size_categories:
  - 1K<n<10K

Boreas Data — Datacenter Digital Twin Surrogate Predictions

Prediction outputs from five neural surrogate models trained on NVIDIA PhysicsNeMo-Datacenter-CFD for Project Boreas (SJSU MSDA capstone, Spring 2026).

This dataset powers the Boreas Omniverse Kit operator console — a custom NVIDIA Omniverse application with an LLM agent (GPT-4o) and Apple Vision Pro AR streaming.

Contents

Folder	What	Count	Size each
test_data/inputs/	10-channel input tensors (80x96x960)	10	282 MB
test_data/targets/	5-channel CFD ground truth (80x96x960)	10	141 MB
outputs/predictions/	Model predictions (5-ch, 80x96x960)	50	141 MB
stl/	OpenFOAM datacenter geometry	6	< 1 MB
results/	Evaluation metrics JSON	2	< 1 MB

Total: 79 files, ~11 GB

Five surrogate models

Model	Family	Params	T MAE (C)	Inference (ms)
U-Net	Spatial (CNN)	22.6M	0.205	177
PI-U-Net	Spatial + physics	344K	0.244	87
FNO	Spectral (FFT)	28.3M	0.489	636
PI-FNO	Spectral + physics	28.3M	0.505	674
Transolver	Transformer	545K	0.598	4,972

Trained at full resolution (960x96x80 = 7.37M points) on NVIDIA GB10 (Grace Blackwell, 128 GB).

File naming

Predictions: outputs/predictions/sample_NNNN_MODEL.npy

• NNNN = 0000-0009 (from PhysicsNeMo test split)
• MODEL = unet, fno, pifno, pi_unet, or transolver
• Shape: (5, 80, 96, 960) — channels: Ux, Uy, Uz, T, p (Z-score normalized)

Denormalization

Field	Mean	Std	Unit
T	39.0	4.0	C
Ux, Uy, Uz	1.5984	1.3656	m/s
p	6.1227	4.1660	Pa

To convert: T_celsius = prediction[3] * 4.0 + 39.0

Quick start with Kit app

git lfs install
git clone https://huggingface.co/datasets/irangareddy/boreas-data ~/boreas-data
git clone https://github.com/irangareddy/kit-app-template.git
cd kit-app-template
.\repo.bat build
.\run_streaming.bat

Quick start in Python

import numpy as np

gt = np.load("test_data/targets/sample_0000.npy")
pred = np.load("outputs/predictions/sample_0000_unet.npy")

T_gt = gt[3] * 4.0 + 39.0
T_pred = pred[3] * 4.0 + 39.0
print(f"Temperature MAE: {abs(T_pred - T_gt).mean():.3f} C")

Source

Derived from nvidia/PhysicsNeMo-Datacenter-CFD (Apache 2.0).

Citation

@misc{boreas2026,
  title={Evaluating Neural Operator Architectures for Datacenter Digital Twin Surrogate Models},
  author={Nukala, Sai Ranga Reddy and Kankanala, Shaila Reddy and Singh, Sadhvi and Jonnalagadda, Akshith Reddy and Shim, Simon},
  year={2026},
  institution={San Jose State University},
  url={https://github.com/irangareddy/298AB}
}

Links

• Research: github.com/irangareddy/298AB
• Kit app: github.com/irangareddy/kit-app-template
• Source data: nvidia/PhysicsNeMo-Datacenter-CFD