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MAXSS & NVIDIA
The document discusses advancements in climate research and AI technologies, highlighting NVIDIA's role in integrating AI with environmental monitoring and simulation. It covers various applications of AI in predicting weather patterns, wildfire detection, and enhancing climate models through novel computational frameworks. Furthermore, it emphasizes the need for high-resolution climate prediction and explores NVIDIA's initiatives, including the Earth-2 project and Fourcastnet model, aimed at leveraging AI to address complex climate challenges.
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MAXSS & NVIDIA
- 1. The bleeding edgeof climate R&D Alison B Lowndes | Senior Scientist | Global AI
- 2. • Quick introto NV • AI + Omniverse • In Mike Pritchard’s absence • Further resources Agenda
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- 6. CONVERGED ACCELERATORS JETSON AGXXAVIER (industrial) developer.nvidia.com/converged-accelerator-devkit
- 7. Next Level ofAI GPGPU in Space Applications Aitech’s S-A1760 Venus™: most powerful and smallest space AI GPGPU in small form factor (SFF). Suitable for the next gen of short duration spaceflight, NEO and LEO. 7
- 8. Accelerated prediction andreal-time detection SATELLITE IMAGERY WEATHER TERRAIN Infrequent updates Minutes/Hours Hours/Days INCOMING SENSOR DATA WILDFIRE PREDICTION MODEL OMNIVERSE REGIONAL DIGITAL TWIN LIST OF COARSE PROBABLE FIRE LOCATIONS OBSERVATIONS EVENT PREDICTION THIRD PARTY PLANNING APPS/OV CLIENTS Fire front simulations Detected, refined fire locations and attributes “State model updates” DATA CENTER EDGE • Coarse resolution • Broad area coverage • Regular updates • Acceleration opportunity • High resolution collection – ‘soda straw’ • Potentially comms-challenged Either published out or offline loaded prior to mission Physics-informed AI Model? *currently, we are working here Accelerated fired detection and orthorectification Edge processing as fast as camera runs • Precision mapping onto terrain • Critical fire location / attributes
- 9. 9 “NVIDIA is proudto have been one of ‘the Mavericks’ at the inception of the Frontier Development Laboratory. Our headquarters are named Endeavor and Voyager – mighty ships for our journey to the stars. We are delighted to support FDL as we push the frontiers for the human race.” JENSEN HUANG, Founder, President & CEO, NVIDIA Corporation
- 11. AI PORTFOLIO Forecast ofmagnetic field perturbation 30 mins in advance for the first time…
- 12. AUTONOMOUS DRIVING SMART FACTORY DESIGN & ENGINEERING CONCEPT & STYLING SOFTWARE& ELECTRONICS RETAIL EXPERIENCE NVIDIA AI NVIDIA Omniverse
- 13. 13 Neural Radiance Fields(NeRFs) introduced in 2020 NeRFs are an astonishing new method for capturing reality NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2020.
- 14. 14 Text Input forLatent Space Embedding Instant-NGP NeRFs + Hashgrid Differentiable Rendering Pure Noise Diffusion Process Final Image Diffusion Models
- 15. OPTION 2 WORLD SIMULATIONIS THE ULTIMATE COMPUTER SCIENCE CHALLENGE VIRTUAL WORLD
- 16. OPTION 2 WORLD SIMULATIONIS THE ULTIMATE COMPUTER SCIENCE CHALLENGE VIRTUAL WORLD Rendering
- 17. OPTION 2 WORLD SIMULATIONIS THE ULTIMATE COMPUTER SCIENCE CHALLENGE VIRTUAL WORLD Physics Rendering
- 18. WORLD SIMULATION ISTHE ULTIMATE COMPUTER SCIENCE CHALLENGE VIRTUAL WORLD Rendering Physics AI
- 19. SUPERCOMPUTING BRINGS WORLDSIMULATION TO AN INFLECTION POINT VIRTUAL WORLD Rendering Physics AI
- 20. Sensors Controllers Humans PHYSICAL WORLD Real-Time Data Synchronization ControlSignal DIGITAL TWINS ARE A NEW CLASS OF WORLD SIMULATION VIRTUAL WORLD Rendering Physics AI
- 21. UNIVERSAL SCENE DESCRIPTIONWILL UNLOCK THE METAVERSE
- 22. Physics Simulation inOmniverse Multiple Ways to Ingest and Simulate Physics in Omniverse NVIDIA PhysX Rigid & Soft Body Dynamics, Destruction, Fluid & Fire Import Physics Instance Offline from External 3rd Party Application NVIDIA Modulus Accelerated with Physics-ML Platform
- 23. DTE USE CASES Alreadyworking with NOAA on a sea-surface system – land, atmosphere, space weather & cryo (ice) coming soon EXTREME WEATHER Predict weather. Detect storms ENERGY Downscale. Predict energy. Optimize. WILDFIRE Satellite detection, fire prediction, smoke forecasting OCEAN AND ICE Predict ice-melt, sea-level, ecosystem changes
- 25. Escalating adventures inusing machine learning To parameterize convection in climate models, and new perspectives from autoregressive weather forecasting in industry Mike Pritchard, Associate Professor at UC Irvine & Director of Climate Simulation Research at NVIDIA.
- 26. The low cloudproblem Projections for 2100 wildly disagree How this system responds to climate change is a multi-trillion dollar question https://fyfluiddynamics.com/2019/06/urban-centers-during-hurricanes/
- 27. • Global StormResolving Models can now capture deep clouds. • The largest scales of vertical mixing. • Grid requirements: dx ~ 1,000m, dt ~ 20s Simulating these systems globally requires unfathomable compute At least 10,000x more than the world’s most computationally demanding global storm-resolving models • Shallow clouds are way harder to simulate • Fine-scale eddies • Grid requirements: dx ~ 100-m, dt ~ 1s • Tens of thousands of times more compute- intensive Deep Cumulonimbus Resolvable with today’s global storm resolving tech Marine Stratocumulus Computationally out of reach
- 28. Lack of sub-kmphysics in climate models should keep us up at night Boundary layer turbulence Where humans live Low cloud formation Aerosol-cloud interaction Planetary mixing & climate dynamics Between boundary & free troposphere The computational problem matters
- 29. High resolution climateprediction is a computational challenge 100km 10 km 1 km 100 m 1980 1990 2000 2010 2020 2030 2040 2050 2060 25 km at 10 min 1 km at 20 s (30,000x COMPUTE) 100m at 0.1 s (500 MILLION X COMPUTE) STORM-RESOLVING LOW CLOUD-RESOLVING Horizontal grid spacing Figure adapted from: Schneider, T., Teixeira, J., Bretherton, C. et al. “Climate goals and computing the future of clouds”. Nature Climate Change 7, 3–5 (2017)
- 30. • MMF: MultiscaleModeling Framework • Thousands of “micro-models” of fast, fine-scale physics. • Embedded in a host global climate model. • Computationally approachable & scalable. • Explicit eddies emerging from appropriate equations. • Also known as "Cloud SuperParameterization" • Convenient scale exchange arteries for machine learning. In academia I try to cope using a multi-scale simulation algorithm: “MMF” The Multi-Scale Modeling Framework approach to global climate modeling Cloud superparameterization A multi-scale modeling framework
- 31. Methods to regionalizeunusually high-resolution MMF have emerged Unexpectedly minimal grid transition artifacts. With load-balancing, yields cost savings of ~ 3x Peng, Pritchard et al., JAMES, 2022.
- 32. Worth finding outto leapfrog Moore’s Law and attain appropriate resolution. 100km 10 km 1 km 100 m 1980 1990 2000 2010 2020 2030 2040 2050 2060 25 km at 10 min 1 km at 20 s (30,000x COMPUTE) 100m at 0.1 s (500 MILLION X COMPUTE) STORM-RESOLVING LOW CLOUD-RESOLVING Horizontal grid spacing Figure adapted from: Schneider, T., Teixeira, J., Bretherton, C. et al. “Climate goals and computing the future of clouds”. Nature Climate Change 7, 3–5 (2017) Can we use ML to sneak sub-km physics into climate models decades ahead of computational schedule?
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- 35. 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: NV’s DDWP, first to be trained at ambitious 0.25-deg global resolution
- 37. Fascinating rate ofincrease of FourCastNet’s weather prediction skill. Individual researchers yielding impressive skill gains from ablations independent of large architecture searches 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. Ablations that seem to matter: • State vector size – adding upper atmosphere helped. • Only to a limit; adding other things has not immediately helped. • Model size: # of transformer blocks & hidden dimension. • Caveat: trades off with inference speed, training cost & memory use. • Open question: Most efficient route to improving DDWP accuracy.
- 38. -- Bjorn Stevens,GTC 2022 Tethering via FCN could solve storage, latency crisis for high-res climate prediction AI nimbly generates details between "checkpoints" saved only infrequently from physics-based climate simulations
- 39. DTE ingredients Modulus nowopen-source | NVIDIA Technical Blog
- 40. 40 Lockheed Martin andNVIDIA Selected by NOAA for Earth Observation Building a Climate Research Data Pipeline with NVIDIA Omniverse Anomaly Detection AI Algorithms Data Fusion Sensor Data Ground Data Satellite Data BUILD PHYSICAL WORLD DIGITAL TWIN DESIGN OPERATE SIMULATE
- 41. 41 EXPLORE OMNIVERSE ENTERPRISE SEEYOU IN OMNIVERSE GET ACCESS TO A FREE TRIAL DEVELOP ON OMNIVERSE DOCUMENTATION docs.omniverse.nvidia.com FORUMS omniverse.nvidia.com/forums TUTORIALS AND WEBINARS omniverse.nvidia.com/tutorials DISCORD discord.gg/nvidiaomniverse
- 42. Getting Started WithNVIDIA AI NVIDIA AI Enterprise Trial Programs Evaluation Software Requirements: NVIDIA-Certified System Free evaluation licenses for on premises POC 90 days to test and experience NVIDIA LaunchPad AI development and deployment trial program Deep dive, hands-on labs for AI practitioners and IT staff Requires ~8 hrs / Access for 2 wks
- 43. The Conference forthe Era of AI and the Metaverse March 20-23, 2023 GTC 2023 Keynote
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