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Description
NVIDIA PhysicsNeMo is a publicly available Python-based deep-learning framework designed for the creation, training, fine-tuning, and inference of physics-AI models that integrate physical principles with data, thereby enhancing simulations, developing accurate surrogate models, and facilitating near-real-time predictions in various fields such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate studies, and digital twin technologies. This framework offers powerful, GPU-accelerated capabilities along with Python APIs that are built on the PyTorch platform and distributed under the Apache 2.0 license, featuring a selection of curated model architectures that include physics-informed neural networks, neural operators, graph neural networks, and generative AI techniques, enabling developers to effectively leverage physics-based causal relationships together with empirical data for high-quality engineering modeling. Additionally, PhysicsNeMo provides comprehensive training pipelines that encompass everything from geometry ingestion to the application of differential equations, along with reference application recipes that help users quickly initiate their development workflows. This combination of features makes PhysicsNeMo an essential tool for engineers and researchers seeking to advance their work in physics-driven AI applications.
Description
NeuralWing serves as a cutting-edge model for real-time neural simulation and design optimization specifically tailored for transonic aircraft aerodynamics. It leverages the most comprehensive 3D transonic wing dataset, derived from 30,000 steady-state CFD simulations that span a 3D wing operating within the transonic regime, incorporating variations in four distinct geometry parameters and two different inflow conditions. By utilizing Emmi’s AB-UPT surrogate model, which has been meticulously trained on this extensive dataset, NeuralWing empowers users to effortlessly alter wing geometries, conduct optimizations, and enhance aerodynamic efficiency within mere seconds. The model is designed to facilitate transonic 3D wing simulations, accommodating variations in geometry and inflow, while offering real-time inference and optimization of design parameters. Users input a geometry mesh in STL format along with speed and angle of attack, and in return, they receive outputs that include pressure, friction, velocity fields, and integral forces such as lift and drag. Geometry meshes are generated dynamically in response to four design parameters, employing a differentiable approach that allows for swift assessment of design modifications. Furthermore, NeuralWing boasts an impressive accuracy rate of 99.5%, making it an invaluable tool for aerodynamics research and development. This level of precision ensures that engineers can trust the results as they iterate on their designs.
API Access
Has API
API Access
Has API
Integrations
PyTorch
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
NVIDIA
Founded
1993
Country
United States
Website
developer.nvidia.com/physicsnemo
Vendor Details
Company Name
Emmi AI
Country
Austria
Website
www.emmi.ai/models/neuralwing
Product Features
Product Features
Engineering
2D Drawing
3D Modeling
Chemical Engineering
Civil Engineering
Collaboration
Design Analysis
Design Export
Document Management
Electrical Engineering
Mechanical Engineering
Mechatronics
Presentation Tools
Structural Engineering