At Astraea Software, we specialize in the Aries 3D Surrogate Model, a cutting-edge solution designed for CAD (Computer-Aided Design), CAM (Computer-Aided Manufacturing), and CAE (Computer-Aided Engineering) simulations. This innovative product leverages advanced AI technology to streamline and enhance your simulation processes, delivering faster and more accurate results.
Discover how the ARIES 3D-Surrogate Model can revolutionize your simulation process and deliver faster results. This demo will guide you through the real-time experience of using our surrogate model to solve simple problems efficiently. To access our 3D Surrogate demo, click the button below.
Current simulations use very complex logic to calculate 3D shapes using inverse matrix and iterative calculations. As a result, there are many problems such as time consuming calculations, divergent results, and difficult settings that can only be performed by dedicated personnel.
Many simulations are likely to be routine in nature. Routine tasks are the areas where AI excels. However, in order to handle 3D with AI, there was a problem that convolution was difficult due to the indefinite number of nodes and edges connecting them in 3D data.
Based on the latest CNN research results, Astraea Software has succeeded in convolution of 3D shape data for the first time in the world.
With this technology, we have been providing products for 3D shape classification and matching. This technology has been newly appropriated to the field of simulation, allowing us to offer new products as surrogate models.
CAE analysis solvers have the following issues.
Aries 3D Surrogate-Model will be an AI-based product to address the above issues.
3D Surrogate-Model is a product developed with the intention of training AI on the simulation results of existing CAE analysis solvers, and then using the trained AI model to perform the same simulation on AI.
Design data for routine tasks trained by AI can be simulated using Aries 3D Surrogate-Model to meet the issues of CAE analysis solvers.
Simulations of new conditions that the AI has not been trained for can be done with the CAE analysis solver, but the results can be used to additionally train the AI to simulate them using the Aries 3D Surrogate-Model.
The features of the Aries 3D Surrogate-Model are as follows.
We are using AI for simulation. Since most simulations are routine tasks, we developed our simulation product with the idea that AI can take care of this part of the work, and people can increase the ratio of their work to other creative tasks.
AI is much faster than CAE analysis solvers in terms of simulation calculation speed.
The factors behind this are listed below.
The following is a comparison of calculation execution times between our CAE analysis solver and the Aries 3D Surrogate-Model.
CAE analysis solvers perform high-load calculations such as complex inverse calculations, and depending on the boundary conditions of the simulation, the simulation may be interrupted by divergence. AI, on the other hand, is a simple multiplicative analogy, so it does not interrupt the simulation due to divergence.
CAE analysis solvers perform high-load calculations such as complex inverse calculations, which may use a large amount of computing resources depending on the size of the simulation and boundary conditions. The consumption of computational resources increases in proportion to the complexity of the simulation. However, since AI is a simple multiplication analogy, the consumption of computational resources does not increase in proportion to the complexity of the simulation.
This section describes the scheme of how AI analogizes the simulation results of 3D shapes.
The surrogate model is achieved by analogizing the simulation with the idea of a graph neural network (GNN), which considers a FE mesh model as a graph.
An FE mesh can be considered as a graph.
Where:
Graph nodes = mesh nodes
Graph edges = element edges
Graph node encodes loads, constraints, current node state (nodal stresses, deformations).
Graph edge encodes properties similar to spring properties such as material, edge length.
We can train graph neural network to predict the next state of a graph given its current state and boundary conditions.
Because AI is an analogous calculation, AI models are used to limit the content and target of the analysis, such as the boundary conditions of the simulation. Therefore, in order to support multiple types of simulations, Aires 3D Surrogate-Model is provided in the following configuration.
You can enter boundary conditions, etc. on the Web, run simulations, and check the results on the Web. Some of the functions of the CAE analysis solver are also available on the Web. It is also possible to download the simulation results.
It is also possible to provide an API that only executes the simulation. However, you will need to modify your own tools.
Also, if it is difficult to modify the tool you are using, please contact us and we will be happy to help you.
We offer the Aries 3D-Surrogate Model software in a versatile package to meet our customers’ needs. This software is available as an executable (.exe) file, providing a user-friendly graphical interface (GUI) upon request. Additionally, for those who prefer or require command-line operations, the software can be executed via command-line interfaces such as CMD, PowerShell, and Bash.
Our solution is designed to be flexible and compatible with various backend operating systems, including Linux and others. This ensures that regardless of your system setup, you can seamlessly integrate and utilize our AI surrogate model software.
Key Features:
By offering both GUI and command-line options, we cater to a wide range of user preferences and technical requirements, making our Aries 3D-Surrogate Model software a versatile tool for your simulation needs.
The Aries 3D Surrogate-Model can be flexibly configured to suit your environment. We can flexibly respond to requests such as starting small to reduce the initial investment.
If you are interested in the Aries 3D Surrogate-Model, please feel free to ask any questions you may have please Contact Us.
