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Design Exploration
Once the baseline model is prepared, you can define morph volumes, morph geometry, create design shapes and run DOE studies. These tool work for both AcuSolve and ultraFluidX-based workflows.
PhysicsAI
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 HyperMesh CFD 2026.
Get Started
Learn the basics and discover the workspace.
Onboarding Resources
Discover HyperMesh CFD functionality with interactive tutorials and community resources.
Solution Layers
Solution-centric workflows allow you to traverse through modeling environments using a dedicated UI layer.
Manage Files and Data
Create, open, import, and save models.
Convert
Use the Convert tool to convert entire models between data types.
Geometry Repair
Manage CAD, FE, and 2D parametric sketch geometry.
Meshing
Generate surface/volume mesh by defining mesh controls, or interactively create and edit 2D surface mesh.
Setup
Prepare your model and run the simulation.
Post-Processing
Post-process the simulation results by creating visualizations and measurements.
Design Exploration
Once the baseline model is prepared, you can define morph volumes, morph geometry, create design shapes and run DOE studies. These tool work for both AcuSolve and ultraFluidX-based workflows.
- Morphing
  Rapidly change the shape of the FE mesh without severely sacrificing the mesh quality.
- PhysicsAI
  - PhysicsAI Capabilities in CFD
    PhysicsAI is an AI-powered tool designed to accelerate simulation workflows by leveraging historical simulation data.
  - Guidelines for PhysicsAI Usage
  - 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.
  - Decimate
    The Decimate tool reduces the number of mesh elements while preserving key features of the geometry, simplifying complex models without sacrificing accuracy.
  - 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.
Automation
Automate simulation processes with templates and utility scripts.
Solver Help
More resources for AcuSolve and ultraFluidX solvers.

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?

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.
Launch and use HyperMesh.
Supported GPUs will be automatically used.

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.