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What is NVIDIA PhysicsNeMo?

NVIDIA's PhysicsNeMo is an open-source deep-learning framework built in Python that facilitates the design, training, fine-tuning, and inference of AI models that marry physical laws with data, thereby improving simulations, creating precise surrogate models, and enabling near-real-time predictions across a variety of domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather forecasting, climate science, and digital twin technologies. It boasts robust GPU-accelerated performance and offers Python APIs based on the PyTorch framework, all distributed under the Apache 2.0 license, featuring a variety of pre-designed model architectures, including physics-informed neural networks, neural operators, graph neural networks, and generative AI methods, allowing developers to effectively harness the causal relationships present in physics along with empirical data for superior engineering modeling. Furthermore, PhysicsNeMo includes extensive training pipelines that cover all aspects from geometry ingestion to the implementation of differential equations, in addition to providing reference application recipes that assist users in rapidly kickstarting their development processes. This unique integration of powerful features positions PhysicsNeMo as a vital resource for engineers and researchers aiming to push the boundaries of physics-based AI applications. Overall, its capabilities make it a crucial asset for anyone looking to innovate in fields that rely on the intersection of artificial intelligence and physical modeling.

What is NVIDIA DIGITS?

The NVIDIA Deep Learning GPU Training System (DIGITS) enhances the efficiency and accessibility of deep learning for engineers and data scientists alike. By utilizing DIGITS, users can rapidly develop highly accurate deep neural networks (DNNs) for various applications, such as image classification, segmentation, and object detection. This system simplifies critical deep learning tasks, encompassing data management, neural network architecture creation, multi-GPU training, and real-time performance tracking through sophisticated visual tools, while also providing a results browser to help in model selection for deployment. The interactive design of DIGITS enables data scientists to focus on the creative aspects of model development and training rather than getting mired in programming issues. Additionally, users have the capability to train models interactively using TensorFlow and visualize the model structure through TensorBoard. Importantly, DIGITS allows for the incorporation of custom plug-ins, which makes it possible to work with specialized data formats like DICOM, often used in the realm of medical imaging. This comprehensive and user-friendly approach not only boosts productivity but also empowers engineers to harness cutting-edge deep learning methodologies effectively, paving the way for innovative solutions in various fields.

Media

Media

Integrations Supported

Caffe
Dask
NetApp AIPod
PyTorch
Python
TensorFlow
Torch
Unleash live

Integrations Supported

Caffe
Dask
NetApp AIPod
PyTorch
Python
TensorFlow
Torch
Unleash live

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/physicsnemo

Company Facts

Organization Name

NVIDIA DIGITS

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/digits

Categories and Features

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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