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~ similar to 2605.28525· 18 results

cs.CERecentMay 29, 2026

On limitations of polyconvexity

Dominik K. Klein, Rogelio Ortigosa, Heinrich T. Roth, Karl A. Kalina +3 more

This paper investigates the limitations of polyconvex constitutive modeling, showing that while theoretically appealing, it can impose overly restrictive constraints and perform poorly in reproducing…

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cs.CVcs.AIcs.GRRecentMay 28, 2026

City-Mesh3R: Simulation-Ready City-Scale 3D Mesh Reconstruction from Multi-View Images

Sayan Paul, Sourav Ghosh, Siddharth Katageri, Soumyadip Maity +2 more

City-Mesh3R is a scalable, end-to-end framework that reconstructs high-fidelity, watertight 3D surface meshes of entire city-scale environments directly from large collections of multi-view images.

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cs.GRcs.CGRecentMay 30, 2026

Subgrid Marching Tetrahedra

Hossein Baktash, Mark Gillespie, Keenan Crane

The paper introduces a subgrid marching tetrahedra scheme that accurately recovers complex, intersection-free manifold meshes from tetrahedral grids, overcoming limitations of classic marching methods…

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cs.PFcs.ARcs.DCRecentMay 28, 2026

From Roofline to Ruggedness: Decomposing and Smoothing the GEMM Performance Landscape

Aditya Chatterjee

The paper introduces performance ruggedness analysis to quantify performance variance in GEMM workloads, proposing a two-stage software stack that significantly smooths the performance landscape and b…

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cs.CVcs.CGRecentMay 28, 2026

S2MDF: A Plug-And-Play Layer for Intersection-Free Multi-Object Signed Distance Fields

Deniz Sayin Mercadier, Federico Stella, Aurel Bizeau, Nicolas Talabot +1 more

The paper introduces S2MDF, a plug-and-play module that enforces a hard constraint to eliminate interpenetrations in multi-object Signed Distance Field (SDF) representations, significantly improving p…

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cs.LGcs.AIRecentJun 1, 2026

FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo

Kyunghun Nam, Sumyeong Ahn

The paper proposes FOAM, an adaptive damping method that stabilizes the Shampoo optimization algorithm by dynamically controlling damping and eigendecomposition frequency, thereby reducing staleness-i…

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cs.AIRecentJun 1, 2026

Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models

Steffen Knoblauch, Hao Li, Gengchen Mai, Konstantin Klemmer +2 more

The paper advocates for a paradigm shift toward joint Spatial Representation Learning (SRL) that unifies raster imagery and structured vector data into a single embedding space for developing more sem…

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cs.LGcs.CEmath.NARecentMay 27, 2026

History-aware adaptive reduced-order models via incremental singular value decomposition

Amirpasha Hedayat, Ali Mohaghegh, Laura Balzano, Cheng Huang +1 more

The paper introduces a history-aware adaptive Reduced-Order Model (ROM) framework using incremental Singular Value Decomposition (iSVD) that maintains accuracy for online dynamics far beyond the initi…

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cs.CVcs.AIRecentMay 30, 2026

GeoSAM-3D: Geodesic Prompt Propagation for Open-Vocabulary 3D Scene Segmentation from Monocular Video

Arun Sharma

GeoSAM-3D proposes a novel framework for open-vocabulary 3D scene segmentation from simple monocular video by propagating object prompts using a geodesic distance kernel on a reconstructed Gaussian sc…

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math.NAcs.CEmath-phRecentMay 28, 2026

Multifidelity Proper Orthogonal Decomposition

Nicole Aretz, Karen Willcox

The paper introduces Multifidelity Proper Orthogonal Decomposition (MFPOD), a method that significantly reduces the computational cost of dimension reduction by intelligently combining data from cheap…

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cs.CVRecentJun 2, 2026

NewtPhys: Do Foundation Models Understand Newtonian Physics?

Sebastian Cavada, Soumava Paul, Tuan-Hung Vu, Andrei Bursuc +1 more

The paper introduces NewtPhys, a novel 4D dataset of real-world scenes with dense physical annotations, to systematically evaluate and reveal the limitations of foundation models in low-level Newtonia…

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cs.CERecentMay 31, 2026

MsFEM-Inspired CNNs with Transfer Learning for Multiscale Model Reduction

Xuehan Zhang, Lijian Jiang, Eric T. Chung

The paper proposes MITL, an MsFEM-inspired transfer learning strategy for CNN-based reduced-order models, enabling efficient and adaptable approximation of multiscale systems with minimal retraining.

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math.NAcs.CEcs.LGRecentJun 1, 2026

Physics-Informed Residuals for Adaptive Mesh Refinement in Finite-Difference PDE Solvers

Henry Kasumba, Ronald Katende

The paper proposes using a Physics-Informed Neural Network (PINN) residual as an efficient, physics-guided indicator to guide adaptive mesh refinement (AMR) for classical finite-difference PDE solvers…

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cs.LGcs.CEmath.NARecentMay 31, 2026

Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs

Lennon J. Shikhman, Shane Gilbertie

The paper introduces Cellular Sheaf Neural Operators, a discretization-aware framework that models constrained PDEs by representing physical states on oriented cell complexes to enforce structure-pres…

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cs.CVcs.AIRecentMay 28, 2026

PhyGenHOI: Physically-Aware 4D Generation of Dynamic Human-Object Interactions

Omer Benishu, Gal Fiebelman, Sagie Benaim

PhyGenHOI introduces a novel framework that generates physically accurate and visually faithful 4D Human-Object Interactions by coupling generative human motion with explicit physical object simulatio…

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cs.AIcs.CERecentMay 27, 2026

VFEAgent: A Multimodal Agent Framework for End-to-End Automated Finite Element Analysis

Jiachen Zhang, Junyi Lao, Chenghao Liu, Siyuan Liu +4 more

VFEAgent is a novel multi-agent framework that automates the entire Finite Element Analysis (FEA) workflow, achieving high success rates in generating complete and physically valid simulations directl…

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cs.AIRecentMay 28, 2026

A Multi-AI-agent Framework Enabling End-to-end Finite Element Analysis for Solid Mechanics Problems

Titu Ranjan Sarker, Muhammed Jawaad Zulqernine, Ling Yue, Shaowu Pan +2 more

The paper introduces AbaqusAgent, a multi-AI-agent framework that uses large language models to translate natural language instructions into executable Finite Element Analysis (FEA) simulations using…

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cs.CVRecentJun 1, 2026

VEDAL: Variational Error-Driven Asynchronous Learning for 3D Gaussian Splatting Pruning

Aoduo Li, Jiancheng Li, Huan Ye, Hongjian Xu +4 more

VEDAL introduces a variational, error-driven asynchronous learning framework to efficiently prune 3D Gaussian Splatting, achieving high compression ratios with minimal loss in novel view synthesis qua…

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