NVIDIA NGCNVIDIA
|
NVIDIA PhysicsNeMoNVIDIA
|
|||||
Related Products
|
||||||
About
NVIDIA GPU Cloud (NGC) is a GPU-accelerated cloud platform optimized for deep learning and scientific computing. NGC manages a catalog of fully integrated and optimized deep learning framework containers that take full advantage of NVIDIA GPUs in both single GPU and multi-GPU configurations. NVIDIA train, adapt, and optimize (TAO) is an AI-model-adaptation platform that simplifies and accelerates the creation of enterprise AI applications and services. By fine-tuning pre-trained models with custom data through a UI-based, guided workflow, enterprises can produce highly accurate models in hours rather than months, eliminating the need for large training runs and deep AI expertise. Looking to get started with containers and models on NGC? This is the place to start. Private Registries from NGC allow you to secure, manage, and deploy your own assets to accelerate your journey to AI.
|
About
NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.
|
|||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||||
Audience
Companies and enterprises interested in a deep learning and scientific computing platform
|
Audience
Researchers, engineers, and developers who need an open source Python AI framework to build, train, fine-tune, and deploy physics-informed machine learning models for simulation, digital twins, and real-time prediction
|
|||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||||
API
Offers API
|
API
Offers API
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
No information available.
Free Version
Free Trial
|
Pricing
Free
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationNVIDIA
Founded: 1993
United States
ngc.nvidia.com
|
Company InformationNVIDIA
Founded: 1993
United States
developer.nvidia.com/physicsnemo
|
|||||
Alternatives |
Alternatives |
|||||
|
|
|
|||||
|
|
|
|||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Deep Learning Features
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
|
||||||
Integrations
PyTorch
Amazon Web Services (AWS)
Domino Enterprise AI Platform
NVIDIA GPU-Optimized AMI
Nutanix Enterprise AI
Python
TensorFlow
|
Integrations
PyTorch
Amazon Web Services (AWS)
Domino Enterprise AI Platform
NVIDIA GPU-Optimized AMI
Nutanix Enterprise AI
Python
TensorFlow
|
|||||
|
|
|