arXiv Machine Learning June 19, 2026 By Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly, Tarek Abdoun, Mostafa E. Mobasher

Adaptive Distance-Aware Trunk Deep Operator Learning for Long-Span Roadway Bridges

Read the original on arXiv Machine Learning →

arXiv:2606. 20015v1 Announce Type: new Abstract: Long-span roadway bridges exhibit highly localized structural responses under vehicular loading, making repeated FE analysis computationally expensive for applications such as influence surface generation and structural digital twins.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

benchmarks

arXiv Machine Learning

Jun 9

Mesh Graph Neural Network Framework for Accelerating Finite Element Simulation for Arbitrary Geometries

arXiv:2606. 08287v1 Announce Type: new Abstract: Finite element analysis (FEA) is essential for structural design but remains computationally expensive, particularly when evaluating multiple design iterations or load scenarios.

By Josiah D. Kunz, Kamal Choudhary

arXiv Machine Learning

Sep 23

DeepFEAv2: Deep Learning for Transient Finite Element Analysis Beyond Structured Meshes

arXiv:2609.26426v1 Announce Type: new Abstract: Finite Element Analysis (FEA) is widely used for transient mechanical simulations, but its high computational cost limits real-time and high-resolution...

By Georgios Triantafyllou, Panagiotis G. Kalozoumis, Dimitris K. Iakovidis

benchmarks

arXiv Machine Learning

Jul 7

Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder

arXiv:2607. 05025v1 Announce Type: new Abstract: Vibration-based damage identification in civil infrastructure is a challenging, ill-posed inverse problem due to measurement noise, sparse sensor arrays, and environmental variability.

By Ana Fernandez Navamuel, A. Javier Omella, Diego Zamora-Sanchez, David Pardo

rag diffusion

arXiv Machine Learning

Jul 31

Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries

arXiv:2605. 00760v2 Announce Type: replace Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network, the DeepONet framework.

By Rodolphe Barlogis, Ferhat Tamssaouet, Quentin Falcoz, St\'ephane Grieu

arXiv AI

Jul 29

SpectONet: A Physics-Guided Spectral Deep Operator Network for Euler-Bernoulli Beam Dynamics

arXiv:2607. 25790v1 Announce Type: cross Abstract: This paper proposes a novel physics-guided spectral deep operator network, termed SpectONet, for solving Euler-Bernoulli beam (EBB) vibration problems.

By Shivani Saini, Ramesh Kumar Vats, Arup Kumar Sahoo

arXiv Machine Learning

Aug 21

CarBench: A Comprehensive Benchmark for Neural Surrogates on High-Fidelity 3D Car Aerodynamics

arXiv:2512. 07847v2 Announce Type: replace Abstract: Benchmarking has been the cornerstone of progress in computer vision, natural language processing, and the broader deep learning domain, driving algorithmic innovation through standardized datasets and reproducible evaluation protocols.

By Mohamed Elrefaie, Dule Shu, Matt Klenk, Faez Ahmed

llms computer-vision nlp benchmarks

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