Generating Random Geometries

This example demonstrates how to generate random geometries using random latent parameters.

Before you begin

• Complete “Building a Generative Design Model” to train a Generative Design model.

• Ensure the model training completed successfully.

Import necessary libraries

import os
import random

import ansys.simai.core as asc
from ansys.simai.core.data.geomai.predictions import GeomAIPrediction, GeomAIPredictionConfiguration

Configure your settings

Update these variables with your specific settings:

ORGANIZATION = "my_organization"  # Replace with your organization name
PROJECT_NAME = "new-bracket-project"  # Replace with your project name
WORKSPACE_NAME = "new-bracket-project #1"  # Typically "{PROJECT_NAME} #{number}"
OUTPUT_DIR = "random_geometries"  # Directory to save generated geometries
NUM_GEOMETRIES = 5  # Number of random geometries to generate

Initialize the client and get the workspace

Connect to the instance:

simai_client = asc.SimAIClient(organization=ORGANIZATION)
geomai_client = simai_client.geomai

Retrieve the trained workspace by its name:

workspace = geomai_client.workspaces.get(name=WORKSPACE_NAME)
print(f"Using workspace: {workspace.name}")

Get the number of latent parameters

The number of latent parameters is defined during model training:

latent_dict = workspace.get_latent_parameters()

nb_latent_params = len(next(iter(latent_dict.values())))
print(f"Workspace uses {nb_latent_params} latent parameters")

Create output directory

Create a directory to save the generated geometries:

output_dir = os.path.join(OUTPUT_DIR, workspace.name)
os.makedirs(output_dir, exist_ok=True)
print(f"Output directory: {output_dir}")

Generate random geometries

Generate geometries by creating random latent parameter vectors. Each latent parameter is randomly sampled from a standard normal distribution.

predictions: list[GeomAIPrediction] = []

print(f"\nGenerating {NUM_GEOMETRIES} random geometries...")
for i in range(NUM_GEOMETRIES):
    # Generate random latent parameters (from standard normal distribution)
    latent_params = [random.gauss(0, 1) for _ in range(nb_latent_params)]

    # Create prediction configuration
    config = GeomAIPredictionConfiguration(
        latent_params=latent_params,
    )

    # Run the prediction
    prediction = geomai_client.predictions.run(config, workspace)
    print(f"Prediction {i + 1}/{NUM_GEOMETRIES}: {prediction.id} started...")
    predictions.append(prediction)

Download generated geometries

for i, prediction in enumerate(predictions):
    # Wait for prediction to complete
    if prediction.wait(timeout=600):  # Wait up to 10 minutes
        if prediction.has_failed:
            print(f"✗ Prediction {i + 1} failed: {prediction.failure_reason}")
            continue

        # Download the generated geometry
        output_path = os.path.join(output_dir, f"random_{i + 1:02d}_{prediction.id}.vtp")
        geomai_client.predictions.download(prediction.id, output_path)
        print(f"✓ Saved geometry to {output_path}")
    else:
        print(f"✗ Prediction {i + 1} timed out")

The downloaded VTP files can be used for:

• Visualization in your usual solver.

• SimAI training data or predictions.

• Further analysis and post-processing.

Tips for better results

• Latent parameters typically range from -3 to +3 for meaningful results.

• Increasing resolution improves the geometry resolution and helps capture finer details. However, this will also increase the prediction time.

• Increase timeout for complex geometries.

• Use the workspace’s latent space statistics (min, max) for better sampling.

Next steps

To generate geometries with more control, see Interpolating Between Geometries.