DeepJEB++: Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation
arXiv:2606.12994v2 Announce Type: replace Abstract: Data-driven engineering design is constrained by the lack of large-scale 3D datasets that pair geometry with physics-based performance labels. In particular, existing 3D data augmentation techniques have limitations in preserving subtle and diverse geometric variations, and it remains difficult to automate the subsequent simulation-labeling process, where boundary conditions vary depending on the generated geometry. We present DeepJEB++, a foundation-model-driven data-augmentation framework that expands a small seed set of jet engine brackets
The increasing maturity of foundation models and the persistent data bottleneck in engineering design are converging to enable new approaches to synthetic data generation.
Large-scale, high-fidelity 3D datasets with physics-based labels are critical for advancing AI in complex engineering fields, accelerating design cycles and innovation.
The ability to generate diverse and labeled 3D engineering datasets using foundation models reduces reliance on costly physical simulations and manual design, democratizing advanced simulation capabilities.
- · AI in engineering design
- · Aerospace and automotive R&D
- · CAD/CAE software providers
- · Foundation model developers
- · Traditional manual design workflows
- · Companies without AI integration strategies
Engineers can rapidly iterate on complex designs with AI assistance, leading to faster product development cycles.
The cost of developing and testing physical prototypes will decrease significantly, enabling more ambitious and complex projects.
AI-driven design processes could lead to the discovery of entirely new material and structural forms with unprecedented performance characteristics.
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Read at arXiv cs.LG