NVIDIA Modulus is a platform designed for developing physics-ML surrogate models across various computational domains, including solid mechanics, fluid dynamics, and climate modeling. It enables rapid simulations through AI-enhanced computations and offers tools like FourcastNet for advanced weather predictions and optimization of engineering models. Users can get started by downloading Modulus, accessing learning resources, and participating in community forums.

1. Physics-ML のためのフレームワーク
NVIDIA Modulus 最新事情
Naruhiko Tan, Solution Architect, NVIDIA Japan

2. • What is NVIDIA Modulus
• Physics-ML use cases
• How to get started
Agenda

3. What is NVIDIA Modulus

4. AI Powered Computational Domains
Computational Eng.
Solid & Fluid
Mechanics,
Electromagnetics,
Thermal, Acoustics,
Optics, Electrical,
Multi-body Dynamics,
Design Materials,
Systems
Earth Sciences
Climate
Modeling,
Weather
Modeling,
Ocean Modeling,
Seismic
Interpretation
Life Sciences
Genomics,
Proteomics
Computational
Physics
Particle Science,
Astrophysics
Computational
Chemistry
Quantum
Chemistry,
Molecular
Dynamics
•Process/Product Design,
Manufacturing, Testing,
•In-Service

5. Using AI for simulations – How?
AI for simulations
• AI has already disrupted the way we think of computation in other domains and mapping to AI unleashes parallelism
• Doing once vs repetitive – learn once and infer over and over
*HPC+AI ってよく聞くけど結局なんなの, 山崎,
GTC2022 テクニカルフォローアップセミナー

6. NVIDIA Modulus
Platform for developing Physics ML surrogate model
• A training and inference pipeline - using
Physics (governing equations) and Data
(simulation/observations)
• Customizable and scalable platform
• Higher level of abstraction for domain
experts
• Build generalized AI surrogates for
parameterized domain
• Near real-time high-fidelity simulation
Modulus EULA – It’s free.

7. Developing digital twins for weather, climate, and energy [S41823]
12
Physics-ML categorization
Physics
Data
Fully data
driven
Inductive
bias
Physics
constrained
Fully physics
driven

8. Modulus Framework - Verification
15
MODULUS FRAMEWORK - VERIFICATION
CFD Solid Mechanics Acoustics
Laminar
Turbulent
16
Electromagnetics Vibrations Turbulence
MODULUS FRAMEWORK - VERIFICATION
Modulus
Modulus
Modulus
15
MODULUS FRAMEWORK - VERIFICATION
CFD Solid Mechanics Acoustics
aminar
bulent
15
MODULUS FRAMEWORK - VERIFICATION
CFD Solid Mechanics Acoustics
Laminar
Turbulent
16
Electromagnetics Vibrations Turbulence
MODULUS FRAMEWORK - VERIFICATION
Modulus
Modulus
Modulus

9. Modulus Framework - Performance
SINGLE GPU: Tensor Core Speed-Up for PDEs
A100 FP32 vs. TF32: Results, Compute Time, Loss

10. Modulus Framework - Performance
MULTI-GPU: Node Scalability

11. Physics-ML use cases

12. NVIDIA'S Earth -2: Digital Twins For Weather and Climate [A41326]
Earth-2 Began by Exploring
Data-Driven Weather Prediction
Scope Global, Medium Range
Model Type Full-Model AI Surrogate
Architecture AFNO (Adaptive Fourier Neural Op.)
Resolution: 25km
Training Data: ERA5 Reanalysis
Initial Condition GFS / UFS
Inference Time 0.25 sec (2-week forecast)
Speedup vs NWP O(104-105)
Power Savings O(104)
FourCastNet

13. NVIDIA'S Earth -2: Digital Twins For Weather and Climate [A41326]
Deuben & Bauer (2018), 6 , 60x30, 1.8K pixels, MLP
WeatherBench, Rasp et al. (2020). 5.625 , 64x32, 2K pixels, CNN
Weyn et al. (2019), 2.5 N.H only, 72x36, 2.6k pixels, ConvLSTM
DLWP, Weyn et al. (2020). 2 , 16K pixels, Deep CNN on Cubesphere/(2021) ResNet
FourCastNet, Pathak et al. (2022), 0.25 , ~1,000,000 Pixels, ViT+AFNO
GNN, Keisler et al. (2022), 1 , 64,000 Pixels, Graph Neural Networks
FourCastNet: A new data-driven weather predictor of unprecedented resolution

14. NVIDIA'S Earth -2: Digital Twins For Weather and Climate [A41326]
We train FCN on ambitious amounts of data on large machines
Thanks to full-stack AI + HPC expertise we train on a growing amount of the world's petabytes of past weather data.
Time to solution decreased from 24+ hours to 67 minutes with model and data parallelism
FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators, Kurth et al. (2022), https://arxiv.org/abs/2208.05419

15. NVIDIA'S Earth -2: Digital Twins For Weather and Climate [A41326]
FCN skill improving with training ambition.
Could it one day outperform deterministic models? We don't yet know the limit.
Skill gap reduced by more than half
w.r.t IFS gold standard
Skill gap reduced by more than half
w.r.t IFS gold standard
Acronym Alert:
ACC: Anomaly Correlation Coefficient (metric of weather skill)
IFS: The Integrated Forecast System, a gold standard weather model
FCN: FourCastNet, our digital twin of weather.

16. Open-Source FourCastNet
Join us in pushing the frontiers of data-driven numerical weather prediction
https://github.com/NVlabs/FourCastNet

17. FourCastNet implementation using Modulus
Included in Modulus examples
https://docs.nvidia.com/deeplearning/modulus/user_guide/neural_operators/fourcastnet.html

18. WIND TURBINE WAKE OPTIMIZATION —
SIEMENS GAMESA
Use Case
§ Developing optimal engineering wake models to optimize wind
farm layouts
§ Simulating the effect that a turbine might have on another when
placed in close proximity
Challenges
§ Generating high-fidelity simulation data from Reynolds-averaged
Navier-Stokes (RANS) or Large Eddy Simulations (LES) can take
over a month to run, even on a 100-CPU cluster.
Solution
§ NVIDIA Omniverse and Modulus enable accurate, high-fidelity
simulations of the wake of the turbines, using low-resolution
simulations as inputs and applying super resolution using AI.
NVIDIA Solution Stack
§ Hardware: NVIDIA A100, A40, RTX 8000 GPUs
§ Software: NVIDIA Omniverse, NVIDIA Modulus
Outcome
§ Approximately 4,000X speedup for high-fidelity simulation
§ Optimizing wind farm layouts in real-time increases overall
production while reducing loads and operating costs.
Demo

19. WIND TURBINE WAKE OPTIMIZATION — SIEMENS GAMESA
Super resolution using AI
https://www.youtube.com/watch?v=mQuvYQmdbtw

20. Super Resolution Net and pix2pixHD Net are now available in Modulus
Included in Modulus examples
https://docs.nvidia.com/deeplearning/modulus/user_guide/intermediate/turbulence_super_resolution.html

21. How to get started

22. Download NVIDIA Modulus, now
https://developer.nvidia.com/modulus

23. Learn how to use NVIDIA Modulus
https://github.com/openhackathons-org/gpubootcamp/blob/master/hpc_ai/PINN/English/python/Start_Here.ipynb

24. User Guide (ex. Defining custom PDEs)
NVIDIA Modulus User Guide
https://docs.nvidia.com/deeplearning/modulus/user_guide/foundational/1d_wave_equation.html#writing-custom-pdes-and-boundary-initial-conditions

25. Ask questions at NVIDIA Modulus technical forum
https://forums.developer.nvidia.com/c/physics-simulation/modulus-physics-ml-model-framework/technical-support/453

26. Contribute to NVIDIA Modulus on GitLab!
*Need to submit GitLab repository access request from https://developer.nvidia.com/modulus-downloads

27. Summary
• NVIDIA Modulus is platform for developing Physics-ML surrogate model
• Physics-ML use cases
• FourCastNet
https://arxiv.org/abs/2208.05419, https://github.com/NVlabs/FourCastNet
• Super Resolution
https://blogs.nvidia.com/blog/2022/03/22/siemens-gamesa-wind-farms-digital-twins/
• How to get started
• Download
https://developer.nvidia.com/modulus
• Learn
https://docs.nvidia.com/deeplearning/modulus/index.html
https://github.com/openhackathons-org/gpubootcamp/blob/master/hpc_ai/PINN/English/python/Start_Here.ipynb
• Ask questions
https://forums.developer.nvidia.com/c/physics-simulation/modulus-physics-ml-model-framework/technical-
support/453
• Get involved
https://developer.nvidia.com/modulus-downloads