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Description
NeuralWing serves as a cutting-edge model for real-time neural simulation and design optimization specifically tailored for transonic aircraft aerodynamics. It leverages the most comprehensive 3D transonic wing dataset, derived from 30,000 steady-state CFD simulations that span a 3D wing operating within the transonic regime, incorporating variations in four distinct geometry parameters and two different inflow conditions. By utilizing Emmi’s AB-UPT surrogate model, which has been meticulously trained on this extensive dataset, NeuralWing empowers users to effortlessly alter wing geometries, conduct optimizations, and enhance aerodynamic efficiency within mere seconds. The model is designed to facilitate transonic 3D wing simulations, accommodating variations in geometry and inflow, while offering real-time inference and optimization of design parameters. Users input a geometry mesh in STL format along with speed and angle of attack, and in return, they receive outputs that include pressure, friction, velocity fields, and integral forces such as lift and drag. Geometry meshes are generated dynamically in response to four design parameters, employing a differentiable approach that allows for swift assessment of design modifications. Furthermore, NeuralWing boasts an impressive accuracy rate of 99.5%, making it an invaluable tool for aerodynamics research and development. This level of precision ensures that engineers can trust the results as they iterate on their designs.
Description
Text2Mesh generates intricate geometric and color details across various source meshes, guided by a specified text prompt. The results of our stylization process seamlessly integrate unique and seemingly unrelated text combinations, effectively capturing both overarching semantics and specific part-aware features. Our system, Text2Mesh, enhances a 3D mesh by predicting colors and local geometric intricacies that align with the desired text prompt. We adopt a disentangled representation of a 3D object, using a fixed mesh as content integrated with a learned neural network, which we refer to as the neural style field network. To alter the style, we compute a similarity score between the style-describing text prompt and the stylized mesh by leveraging CLIP's representational capabilities. What sets Text2Mesh apart is its independence from a pre-existing generative model or a specialized dataset of 3D meshes. Furthermore, it is capable of processing low-quality meshes, including those with non-manifold structures and arbitrary genus, without the need for UV parameterization, thus enhancing its versatility in various applications. This flexibility makes Text2Mesh a powerful tool for artists and developers looking to create stylized 3D models effortlessly.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
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
Emmi AI
Country
Austria
Website
www.emmi.ai/models/neuralwing
Vendor Details
Company Name
Text2Mesh
Website
threedle.github.io/text2mesh/
Product Features
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