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ease
features
design
support

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Description

Everstar is an innovative AI-driven platform aimed at enhancing the deployment of nuclear power by leveraging document intelligence, offering licensing assistance, managing regulatory workflows, conducting engineering analysis, and automating nuclear compliance processes. Tailored for nuclear industry professionals, Everstar empowers teams to transform intricate documents into actionable insights, enabling them to create filings that meet regulatory standards, quickly identify relevant precedents, shorten siting timelines, and optimize operations across the entire nuclear value chain while maintaining safety as a top priority. With its advanced AI capabilities, Everstar addresses some of the most challenging administrative, regulatory, and engineering obstacles in the field, ensuring accuracy, traceability, and secure operational workflows. The platform is versatile enough to cater to various stakeholders, including those involved in operating reactor licensing, reactor management and engineering, new reactor approvals, radioactive materials handling, as well as manufacturing and supply chain departments, along with regulatory bodies. This comprehensive support fosters collaboration among different sectors of the nuclear industry, ultimately leading to more efficient processes and safer outcomes.

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.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

PyTorch
Python

Integrations

PyTorch
Python

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

Everstar

Founded

2024

Country

United States

Website

everstar.ai/

Vendor Details

Company Name

NVIDIA

Founded

1993

Country

United States

Website

developer.nvidia.com/physicsnemo

Product Features

Product Features

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