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A Controllable and Compact Reduced-Order Model for Vehicle Engineering Design and Optimization
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DOI：10.1002/nme.70337.png)
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Abstract
En 中文
Engineering design workflows such as early-stage vehicle development require controllable and interpretable parameterizations of complex 3D geometry, enabling designers to modify physically interpretable geometric attributes while keeping unrelated features stable. However, latent representations used in modern implicit shape models often exhibit interdependent coupling among multiple geometric factors, limiting their suitability as independent design variables. Motivated by reduced-order modeling (ROM) and parametric shape design, we propose a geometrically structured latent subspace for signed distance function (SDF) representations that aligns latent directions with human-interpretable geometric attributes (e.g., vehicle height, width, wheelbase) while keeping residual variation compact without prespecifying subspace dimension. The resulting formulation provides a numerically stable, geometry-aware reduced-order parameterization suitable for downstream engineering tasks. The model is trained end-to-end using lightweight global and local alignment losses together with a rank-agnostic compactness regularizer. Anchor directions and normalized traversal step sizes are learned to enable monotonic, predictable edits with reduced cross-attribute interference. Experiments on standard vehicle benchmarks demonstrate that the method preserves SDF reconstruction accuracy while producing attribute-aligned and interpretable latent variations. These results indicate that embedding minimal geometric structure into SDF latent spaces substantially improves controllability, stability, and geometric consistency for vehicle-centric 3D modeling and design exploration.
Keywords:
design space reduction
implicit neural representations
reduced-order modeling
shape optimization
shape representation
surrogate modeling
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