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arXiv:2607. 09763v1 Announce Type: cross Abstract: Engineering shape optimization faces challenges in both expert-dependent problem setup and surrogate-model reliability.
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.
arXiv:2609.36806v1 Announce Type: new Abstract: Modern engineering systems, from automobiles to aircraft, are designed by using precise, continuous parametric computer-aided design (CAD) models. Eval...
arXiv:2609.36806v2 Announce Type: replace Abstract: Modern engineering systems, from automobiles to aircraft, are designed by using precise, continuous parametric computer-aided design (CAD) models....
arXiv:2606. 04850v1 Announce Type: cross Abstract: Designing a neural network processor is an end-to-end co-design problem: network architecture and training budget determine the inference workload; hardware mapping decisions determine chip area, latency, and energy; and these characteristics govern fabrication yield and manufacturing cost.
arXiv:2512. 07847v2 Announce Type: replace Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols.