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The paper introduces the simultaneous neural topology optimization (SNTO) framework, which uses custom neural networks to jointly represent state, adjoint, and design variables in a single optimization loop. SNTO incorporates a multi-component loss function that balances design objectives, aggregated stress constraints, and potential energy, and employs a hardware‑aware training scheme that partitions the design domain across multiple GPUs to handle millions of degrees of freedom. Validation on 2D benchmarks shows super‑resolution designs with sharper boundaries and lower volume fractions, while large‑scale 3D tests demonstrate scalability to eight million collocation points on eight GPUs.
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.
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.
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.
Batch Before You Lift: Scalable Topological Deep Learning on Large Graphs introduces Cluster‑TNN, a framework that lifts graph data locally instead of globally, enabling efficient mini‑batch construction for higher‑order domains such as hypergraphs and simplicial complexes. By partitioning the input graph and dynamically sampling node clusters at runtime, Cluster‑TNN reduces peak GPU memory usage by an average of 83.2% across 21 comparisons while preserving connectivity needed for topological structures. This approach allows the first training of multiple higher‑order Topological Neural Networks on large datasets like Reddit and OGBN Products, demonstrating a general strategy for scaling Topological Deep Learning beyond global domain construction.
arXiv:2606. 19921v1 Announce Type: new Abstract: This work proposes an element-based Convolutional Neural Network (CNN) to accelerate density-based Topology Optimization (TO), termed eCNNTO.