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PhysicsAI
Use PhysicsAI to build fast predictive models from CAE data. PhysicsAI can be trained on data with any physics or remeshing and without design variables.
Frequently Asked Questions
This section provides quick responses to typical and frequently asked questions regarding PhysicsAI.
Computing and Resources

Welcome
What's New
View new features for Altair HyperWorks 2025.1.
Get Started
Learn the basics and discover the workspace.
Tutorials
Learn more about the Altair HyperWorks suite of products with interactive tutorials.
Startup Procedures and Customizations
Start and configure the applications.
Tool Finder
View a list of deprecated panels and their newer, equivalent workflows.
Manage Files and Data
Create, open, import, and save models.
Sessions and Report Templates
Set up sessions and create report templates.
Interface with External Products
Solver interfaces supported in HyperMesh.
Entities and Solver Interfaces
A solver interface is made up of a template and a FE-input reader.
Browsers
Browsers provide a structured view of model data, which you can use to review, modify, create, and manage the contents of a model. In addition to visualization, browsers offer features like search, filtering, and sorting, which enhance your ability to navigate and interact with the model data.
Sketching
Create and edit 2D parametric sketch geometry.
Topology
Create, edit, and cleanup geometry.
FE Geometry
FE geometry is topology on top of mesh, meaning CAD and mesh exist as a single entity. The purpose of FE geometry is to add vertices, edges, surfaces, and solids on FE models which have no CAD geometry.
Meshing and Elements
Explore the different types of mesh you can create in HyperMesh and create and edit 0D, 1D, 2D, and 3D elements.
Model Build and Assembly
Create, organize and manage parts and subsystems.
Composites
HyperMesh composites modeling.
Connectors
Create connections between parts of your model.
Morph
Rapidly change the shape of the FE mesh without severely sacrificing the mesh quality.
Skeleton Modeling
Create a reduced ordered model to facilitate optimization at the concept phase.
Design Space
Workflow to support topology optimization model build and setup.
Optimization
Setup an Optimization in HyperMesh.
Design Explorer
Multi-disciplinary design exploration and optimization tools.
Validate
Validate the model built before running solver analysis.
Analyze
Models require loads and boundary conditions in order to represent the various physics and/or physical equivalents to bench and in-use testing.
Axisymmetry
Reduce a full 3D model with axisymmetric surfaces while accounting for imperfections.
Concept Modeling
Tools and workflows that are dedicated to rapidly creating new parts for specific use cases, or amending existing parts. The current capabilities are focused on stiffening parts.
Crash and Safety
Tools used for crash and safety analysis.
Airbag
Use airbag folder utilities and export a resulting airbag in a Radioss deck.
Aerospace
Essential utility tools developed using HyperMesh-Tcl.
Aeroelasticity
Import an aeroelastic finite element model with Nastran Bulk Data format.
Certification
Framework to plug certification methods to assess margin of safety from the model and result information.
PhysicsAI
Use PhysicsAI to build fast predictive models from CAE data. PhysicsAI can be trained on data with any physics or remeshing and without design variables.
- Load PhysicsAI Ribbon
- Create and Load Projects
  Each session starts with creating a new project or loading an existing project.
- Create and Manage Datasets
  Datasets are collections of one or more simulation results used for training or testing a model.
- Train Models
  Train models using any physics, any mesh, and without design variable parametrization.
- Test Models
  Use the Model Testing tools to test models and review test results.
- Predict Results
  Use the predict tool to generate a model prediction.
- Similarity Score
  Like all machine learning models, PhysicsAI models are most accurate when the design being predicted is similar to the designs used for training.
- Use Batch Mode
  Learn more about using batch mode.
- Best Practices for PhysicsAI
  Learn more about the best practices for training and using PhysicsAI models.
- Frequently Asked Questions
  This section provides quick responses to typical and frequently asked questions regarding PhysicsAI.
Results Data in HyperMesh and HyperView
Results data can be post-processed using both HyperMesh and HyperView.
Developer
The Developer ribbon contains tools for automation and customization.
Plot Data in HyperMesh and HyperGraph
Plot data can be post-processed using both HyperMesh and HyperGraph.
Multibody Modeling in MotionView
MotionView is a general pre-processor for Multibody Dynamics.
Media Files in MediaView
MediaView plays video files, displays static images, tracks objects, and measures distances.
Tabulate Results in TableView
Use TableView to create an Excel-like spreadsheet.
Text Files in TextView
TextView math scripts reference vector data from HyperGraph windows to automate data processing and data summary.
Publishing and Reporting
Create, define, and export reports.

View All Altair HyperWorks Help

Computing and Resources

Is a GPU required?

A CPU can be used to train a model, but it will be slower than a GPU.

What GPUs are supported?

To train with a GPU, you must install CUDA toolkit 11.8 and cuDNN 8.7. This requires a NVIDIA GPU that has a compute capability > 6.0. See the NVIDIA website for more information on the compute capability of specific hardware.

How do I enable NVIDIA GPUs for PhysicsAI training?

Verify the GPU is supported.
This requires a NVIDIA GPU that has a compute capability > 6.0. See the NVIDIA website for more information on the compute capability of specific hardware.
Install CUDA toolkit 11.8 and cuDNN 8.7.
Update the "path" environment variable to include the following directories (this may be different on your machine).
For example, on a Windows PC, the following paths need to be added.
- C:\Program Files\NVIDIA\CUDNN\v8.7\bin
- C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin
- C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp
Relaunch HyperMesh.

What effect does GPU and dataset size have on training times?

The quality of computing resources will affect training time. Better hardware can improve run times, but improvements from CPU to GPU can be substantial. Regardless, training time is linearly proportional to dataset size. See the table below for a representative example:


Hardware	10 Result Files	50 Result Files	100 Result Files
Laptop CPU	44 m	3 h 35 m	7 h 54 m
HPC CPU	34 m	2 h 46 m	5 h 10 m
GPU	3 m	16m	33 m

Can I train on an HPC?

Yes, more information is available here.