Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural topology optimizatio...
The paper introduces SNTO, a simultaneous neural topology optimization framework that represents state, adjoint, and design variables with custom neural networks optimized in a single loop. It employs a hardware‑aware training scheme that partitions the design domain across multiple GPUs and slices subdomains to manage memory, enabling stress‑constrained topology optimization at millions of degrees of freedom. Experiments on 2D benchmarks show sharper boundaries and reduced gray areas compared to finite element methods, while 3D problems with up to eight million points are solved efficiently on eight GPUs.
By Xiangyu Sun, Shirin Hosseinmardi, Ramin Bostanabad
KATOsuper is an objective‑agnostic framework that accelerates neural topology optimization by coupling neural‑reparameterized TO with a Sensitivity‑Consistent Fourier Neural Operator (SC‑FNO). It uses a forward_split architecture to ensure that sensitivities derived via automatic differentiation remain consistent with predicted objectives, enabling stable optimization. The method demonstrates significant deployment‑time speedups (15–110×) over MATLAB baselines while preserving optimality across 2D and 3D benchmark problems, including compliance and stress minimization, and supports zero‑shot extrapolation to higher resolutions.
By Shengyu Yan, Jasmin Jelovica
arXiv:2602. 19330v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are increasingly explored for physical design analysis in Electronic Design Automation, particularly for modeling Clock Tree Synthesis behavior such as clock skew and buffering complexity.
By Barsat Khadka, Kawsher Roxy, Md Rubel Ahmed
arXiv:2609.14841v1 Announce Type: new
Abstract: Scientific machine learning methods such as physics-informed neural networks (PINNs) increasingly rely on domain decomposition for better scalability w...
By Sidharth S. Menon, Irina Tezaur, Ameya D. Jagtap
The paper presents a modified physics-informed DeepONet (PI-DeepONet) architecture that reduces computational cost by eliminating redundancy in the Branch network’s handling of input functions across collocation points. The new formulation is mathematically equivalent to the original but uses smaller input tensors and fewer network evaluations, leading to faster training while maintaining comparable predictive accuracy on 1D, 2D, and 3D linear advection problems. Experiments show up to a 3.75× speed‑up in 2D and enable feasible training of 3D models on an NVIDIA A100 GPU, where the original architecture fails due to memory limits.
By Nahum Banks, Gustavo A. M. de Almeida, Sergio Maldonado