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Average Ratings 0 Ratings

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ease
features
design
support

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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

Simcenter MAGNET serves as an advanced simulation tool for analyzing electromagnetic fields, enabling users to predict the performance of various components such as motors, generators, sensors, transformers, actuators, and solenoids that involve permanent magnets or coils. By facilitating low-frequency electromagnetic field simulations, Simcenter MAGNET offers comprehensive modeling capabilities that accurately represent the underlying physics of electromagnetic devices. Among its features are the modeling of manufacturing processes, temperature-sensitive material properties, and the intricate behavior of magnetization and de-magnetization, along with vector hysteresis models. The software’s built-in motion solver incorporates a six-degree-of-freedom functionality, which allows for the precise modeling and analysis of complex scenarios such as magnetic levitation and intricate motion dynamics. This advanced capability is bolstered by innovative smart re-meshing technology, ensuring that even the most challenging electromagnetic problems can be effectively addressed. Consequently, Simcenter MAGNET stands out as an essential tool for engineers and designers looking to optimize electromagnetic systems in a range of applications.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

PyTorch
Python
Simcenter Amesim

Integrations

PyTorch
Python
Simcenter Amesim

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

Siemens

Country

United States

Website

plm.sw.siemens.com/en-US/simcenter/electromagnetics-simulation/magnet/

Product Features

Product Features

Simulation

1D Simulation
3D Modeling
3D Simulation
Agent-Based Modeling
Continuous Modeling
Design Analysis
Direct Manipulation
Discrete Event Modeling
Dynamic Modeling
Graphical Modeling
Industry Specific Database
Monte Carlo Simulation
Motion Modeling
Presentation Tools
Stochastic Modeling
Turbulence Modeling

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