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What is XGtd?

XGtd is a sophisticated software designed for electromagnetic analysis, employing ray-based techniques to assess the impact of vehicles or vessels on antenna radiation, predict antenna coupling, and calculate radar cross-section. This software is particularly beneficial for high-frequency applications or large platforms, efficiently managing situations where full physics-based methods would require excessive computational resources. In addition to traditional ray tracing, XGtd incorporates advanced methodologies such as Geometric Optics (GO), the Uniform Theory of Diffraction (UTD), Physical Optics (PO), and the Method of Equivalent Currents (MEC). The software stands out by providing accurate and tailored results for its designated applications, delivering high-fidelity field predictions, even in challenging shadow zones affected by creeping wave phenomena. Furthermore, XGtd offers detailed multipath analysis that takes into account a variety of elements, including reflections, transmissions, wedge diffractions, surface diffractions, and creeping waves, thereby enhancing its value in electromagnetic research. Its comprehensive capabilities and accuracy facilitate a nuanced understanding of intricate interactions within demanding environments, making it a crucial asset for engineers and researchers alike.

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.

Media

Media

Integrations Supported

PyTorch
Python

Integrations Supported

PyTorch
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
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

Remcom

Date Founded

1994

Company Location

United States

Company Website

www.remcom.com/xgtd-high-frequency-em-anaysis-software

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/physicsnemo

Categories and Features

Categories and Features

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