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Generating infinite meaningful designs

Design
generation

Automation in Manufacturing
[ Design Generation ]

AI-Based Design Generation

Generative Design
Engineering Design
and Evaluation
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The design generation stage combines the strengths of
physics-based generative design and data-driven deep learning
to automatically generate large numbers of designs that are both engineering-sound and aesthetically meaningful.

In particular, topology optimization and parametric design techniques in engineering design
can be organically integrated with deep learning to create powerful synergies.

Generates infinite
meaningful designs

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[ 1 ]

Seed

Set the initial design geometry and reference model.

[ Design Generation ]
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    From Seed Data to Data Synthesis

    Parametric design, topology optimization, and deep learning
    are combined to generate seed data and synthetic data.
    Data is generated considering both aesthetics and engineering performance.

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    Robust Even with Small Data

    Thousands of data samples are typically required for AI training.
    Yet real-world usable 3D data may number only in the dozens.
    Narnia Labs' generator enables
    data synthesis even with a small amount of data.

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    Exploring a Wide Design Space

    Human-defined parameterization struggles to express
    diverse designs. Parameters extracted by the generator from data
    can represent diverse designs, enabling exploration of a
    broader design space.