arXiv AI
Jun 2
FlowPlace: Flow Matching for Chip Placement
arXiv:2604. 23658v2 Announce Type: replace-cross Abstract: Chip placement plays an important role in physical design.
Physics-Guided Geometric Diffusion for Macro Placement Generation
arXiv:2605. 16451v2 Announce Type: replace-cross Abstract: Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance.
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arXiv Machine Learning
Jul 9
HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization
arXiv:2607. 07233v1 Announce Type: new Abstract: Deep generative models offer a promising paradigm for topology optimization, enabling rapid design exploration.
arXiv Machine Learning
Jun 2
Real-Time Sensing of Inaccessible Physical Fields via an Edge-Deployable Hardware-Portable Graph Neural Operator
arXiv:2604. 01802v2 Announce Type: replace Abstract: Real-time inference of inaccessible interior physical fields from sparse boundary observations is a fundamental but unresolved problem in scientific machine learning, with direct relevance to safety-critical monitoring across many engineering applications.
arXiv Machine Learning
Jul 10
PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations
arXiv:2607. 08025v1 Announce Type: new Abstract: While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by high memory consumption and the single node bottleneck, where the maximum processable mesh resolution is strictly limited by the VRAM of a single compute unit.
arXiv Machine Learning
Jun 30
Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
arXiv:2602. 04940v2 Announce Type: replace Abstract: Deep learning has emerged as a transformative tool for the neural surrogate modeling of partial differential equations (PDEs), known as neural PDE solvers.
arXiv Machine Learning
Jun 15
Smoothing Dark Areas in Molecular Latent Diffusion
arXiv:2606. 13955v1 Announce Type: new Abstract: Latent diffusion is a promising framework for scalable 3D molecular generation, but it requires a latent space that remains smooth, valid, and navigable beyond posterior samples.
Hugging Face Trending Papers
Jul 9
PGD-NO: A Neural Operator with Precomputed Geometry Decomposition for 3D Million-scale Physics Simulations
While neural PDE solvers have demonstrated significant potential for accelerating engineering simulations, existing architectures remain constrained by high memory consumption and the single node bottleneck, where the maximum processable mesh resolution is strictly limited by the VRAM of a single compute unit. To address these challenges, we propose PGD-NO, a neural operator with Precomputed Geometry Decomposition, that relocates the computational overhead of geometric encoding to a deterministic pre-computation phase.
arXiv Machine Learning
Jun 8
Drifting Models for Surrogate Flow Modeling
arXiv:2606. 07481v1 Announce Type: new Abstract: While Computational Fluid Dynamics (CFD) provides high-fidelity flow fields for optimizing indoor environments, its computational cost limits rapid exploration.
arXiv Machine Learning
Jul 16
EXPLORE: Exploration with Guided Search for Analog Topology Generation using Language Models
arXiv:2607. 13416v1 Announce Type: new Abstract: Automating analog circuit topology design is essential to reduce the extensive manual effort required to meet increasingly diverse and customized application demands.
arXiv Machine Learning
Jun 19
Performance Analysis and Optimization of 3D Generative Diffusion Models across GPU Architectures
arXiv:2606. 19365v1 Announce Type: new Abstract: Diffusion models have become essential for high-fidelity 3D MRI synthesis, yet their deployment remains constrained by substantial GPU resource demands arising from hundreds of U-Net evaluations per sample and a highly heterogeneous kernel behavior.
arXiv Machine Learning
Jun 2
Efficient Diffusion Models under Nonconvex Equality and Inequality constraints via Landing
arXiv:2604. 17838v2 Announce Type: replace Abstract: Generative modeling within constrained sets is essential for scientific and engineering applications involving physical, geometric, or safety requirements (e.
arXiv AI
Jun 16
Separable Neural Architectures as Physical World Models: from Mathematical Theory to Applications
arXiv:2606. 14934v1 Announce Type: cross Abstract: This work introduces the Separable Neural Architecture (SNA), a function representational class combining neural approximation with tensor decomposition.