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arXiv Machine Learning By Suryanarayanan Manoj Sanu, Miguel Anibal Bessa, Alejandro Marcos Arag\'on

Bilevel Optimization of Topology and Hyperparameters (BOTH)

Read the original on arXiv Machine Learning →

The paper introduces BOTH, a method that differentiates topology optimization (TO) itself to compute hypergradients for tuning hyperparameters alongside the primary design optimization. By evaluating only one or two TO steps, the approach provides sufficient information and scales to thousands of hyperparameters with a cost comparable to a few standard TO runs. Experiments on stress‑constrained and compliance problems, including a neural‑parameterized density field, demonstrate the effectiveness of this joint optimization strategy.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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