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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 Differential Bio?

Differential Bio is a cutting-edge virtual platform aimed at improving bioprocess optimization by enhancing the predictability, scalability, and cost-effectiveness of microbial growth through the use of robotic lab automation and AI-driven insights. The journey begins with automated, high-throughput experiments that simulate large-scale stress conditions in microliter volumes, generating valuable wet-lab data through techniques in liquid handling, fermentation, and measurement. Custom AI algorithms meticulously analyze this information to predict various outcomes such as biomass growth, protein synthesis, cell viability, titer, production rates, yields, and cost implications. Accessible via a web-based interface, users can define their optimization goals and parameter thresholds, carry out unlimited in-silico simulations, assess the outcomes of simulated fermentation processes, engage in multi-objective optimization, and determine the best conditions for scaling production. This holistic methodology not only optimizes the workflow but also equips teams with the ability to make informed, data-driven decisions in the realm of bioprocess development, thereby promoting innovation and efficiency in the field. By integrating advanced technologies, Differential Bio sets a new standard for the future of bioprocessing.

Media

Media

Integrations Supported

PyTorch
Python

Integrations Supported

PyTorch
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

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

Differential Bio

Company Location

Germany

Company Website

www.differential.bio/

Categories and Features

Categories and Features

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