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 Computer Vision
Sep 2
ASSERT: Adaptive Stochastic Sampling for Robust Diffusion Models on Analog Compute-in-Memory Hardware
arXiv:2609.00955v1 Announce Type: new Abstract: Diffusion models achieve strong image generation quality but incur high iterative denoising costs. Analog compute-in-memory (CIM) can accelerate matrix...
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.
arXiv AI
Sep 3
GeoSPRINT: Geometric Redundancy-Aware Step Pruning for Inference in Diffusion Trajectories
GeoSPRINT is a training‑free framework that constructs non‑uniform sampling schedules for diffusion model inference by detecting geometrically redundant steps in denoising trajectories. It uses a hyperplanarity test in latent space, implemented via QR factorization, to allocate more steps to high‑curvature regions, and introduces the trajectory projection score α_traj as a model‑free diagnostic for flow quality. Across CIFAR‑10, LSUN Church, and Stable Diffusion v1.5, GeoSPRINT consistently outperforms uniform DDIM schedules at matched NFE budgets, improving FID scores by up to 1.93 points.
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 AI
2d ago
SILSA: Sliding-Window Slice Latents for Topology-Preserving High-Resolution 3D Generation
arXiv:2610.02201v1 Announce Type: cross Abstract: High-resolution 3D generation increasingly relies on voxel latents and multi-stage pipelines that first predict active structure and then synthesize...
arXiv Machine Learning
4d ago
GRIFDIR: Graph Resolution-Invariant Diffusion Models over Irregular Domains
GRIFDIR is a new architecture for score-based diffusion models that operates directly on unstructured meshes, enabling function-space diffusion over irregular domains. By representing generalized graph convolutional kernels as finite element functions, the model achieves resolution invariance and can handle complex, non-convex, and multiply-connected geometries. Experiments demonstrate that GRIFDIR maintains high fidelity in both unconditional and conditional sampling across diverse shapes.
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.