Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
PhysicsX Platform is an advanced engineering platform driven by AI that harnesses the capabilities of artificial intelligence throughout the domains of design, manufacturing, and operations, facilitating significant advancements in performance, efficiency, and speed for vital industrial sectors globally. This platform merges simulation, physics AI, data, and engineering applications into a cohesive software foundation that seamlessly integrates with existing tools utilized by engineers. Specifically designed to support the entire product lifecycle—from initial concept and design to manufacturing and operational phases—PhysicsX incorporates AI into the engineering process rather than treating it as an additional component. By empowering enterprises to swiftly create, implement, and expand a new generation of AI tools within intricate engineering workflows, PhysicsX allows teams to effectively train and deploy physics AI directly in real-world design and production contexts while enhancing productivity and innovation. Ultimately, the platform represents a transformative step toward the future of engineering.
API Access
Has API
API Access
Has API
Integrations
ANSYS SpaceClaim
Abaqus
Amazon Web Services (AWS)
CATIA
Microsoft Azure
PyTorch
Python
Siemens NX
Simcenter STAR-CCM+
Integrations
ANSYS SpaceClaim
Abaqus
Amazon Web Services (AWS)
CATIA
Microsoft Azure
PyTorch
Python
Siemens NX
Simcenter STAR-CCM+
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
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
PhysicsX
Country
United Kingdom
Website
www.physicsx.ai/platform
Product Features
Product Features
Artificial Intelligence
Chatbot
For Healthcare
For Sales
For eCommerce
Image Recognition
Machine Learning
Multi-Language
Natural Language Processing
Predictive Analytics
Process/Workflow Automation
Rules-Based Automation
Virtual Personal Assistant (VPA)
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
Alternatives
Alternatives
No Alternatives