NVIDIA PhysicsNeMo 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.

Pricing

Pricing Starts At:
Free
Free Version:
Yes

Integrations

API:
Yes, NVIDIA PhysicsNeMo has an API

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Company Details

Company:
NVIDIA
Year Founded:
1993
Headquarters:
United States
Website:
developer.nvidia.com/physicsnemo

Media

NVIDIA PhysicsNeMo Screenshot 1
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Product Details

Platforms
Web-Based
Types of Training
Training Docs
Live Training (Online)
Webinars
Training Videos
Customer Support
Business Hours
Online Support

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