Applied Cases

Geophysics

NVIDIA Earth-2 enables weather-sensitive industries like energy to make timely decisions by integrating observational data with AI-powered atmospheric simulation.

Thermal Management / Physics-Informed ML

A physics-informed convolutional neural network whose loss encodes the finite-difference heat conduction equation, used as a surrogate for the temperature field of a liquid-cooled battery pack and more accurate than a purely data-driven model trained on the same data.

Flow Control / Turbulent Drag Reduction

An open-source reinforcement learning platform for flow control that couples six CFD solver backends with dozens of pre-configured environments; trained policies transfer zero-shot to a wing and cut skin-friction drag substantially.

Molecular Dynamics / ML Interatomic Potential

A general-purpose machine-learned interatomic potential built on an equivariant graph neural network, trained on 150k public inorganic crystals, that runs stable molecular dynamics out of the box for solids, liquids, gases and reactions, and can be fine-tuned to ab initio accuracy with only a handful of configurations.

Structural Optimization / Generative Design

A conditional diffusion approach to structural topology optimization from MIT that uses surrogate-model guidance to optimise both mechanical compliance and manufacturability, outperforming conditional GANs by a wide margin.

AI Simulation

This NVIDIA blog post describes how agentic AI workflows can be used to prepare and validate digital twins for physical AI systems, with agents capable of inspecting 3D scenes and authoring simulation-relevant data.

AI Inference Optimization

NVIDIA Groq 3 LPX leverages deterministic execution technology to enhance inference efficiency on the Vera Rubin platform, enabling high-interactivity and power-efficient computing for AI factory workloads.

Aerodynamic Design / Surrogate Model

Airfoil aerodynamic simulation and surrogate study: large parametric CFD datasets of NACA airfoils plus FNO/DeepONet surrogate comparison.

Automotive Aerodynamics / Drag Prediction

Train surrogates on high-fidelity LES data for millisecond drag-coefficient (Cd) prediction, enabling digitalization and aero optimization of vehicle external flow.

Structural Health Monitoring / Digital Twin

Real-time SHM digital twin of a long-span suspension bridge via PINN, fusing strain measurements with physics constraints for damage detection and remaining-life prediction.

Wind Energy / Load Prediction

Data-driven prediction of blade fatigue load and power curve, solving hard-to-evaluate turbulent loads under extreme winds - for farm O&M decisions.

Biomedical Flow / Reduced-Order Model

Neural networks predict 0D reduced-order model parameters from geometry, cutting cardiovascular flow error by over half and running complex pulmonary cases in under 2 seconds on a laptop.

Materials Science / First Principles

Google DeepMind's GNN system for materials discovery predicting 2.2M new crystal structures with 380k stable ones, expanding known stable materials by nearly an order of magnitude.

Geophysics / Inverse Problem

LANL's large-scale FWI benchmark - 12 datasets, 2.1 TB - reconstructing subsurface velocity from seismic data with data-driven methods, serving carbon storage, reservoir and earthquake applications.

Weather / Large-Scale Physical Prediction

A graph neural network for medium-range weather prediction that outperforms conventional numerical weather prediction on many targets while running in minutes instead of hours.

Energy / Fusion Control

Deep reinforcement learning directly commands tokamak magnetic coils to hold diverse plasma configurations, a landmark public case of AI controlling a real physical device.