arXiv Machine Learning

A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets

arXiv:2606. 27304v1 Announce Type: new Abstract: Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures.

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Jun 25

A Multi-Fidelity Convolutional Autoencoder-Transfer Learning Framework for Guided-Wave-Based Damage Diagnosis Using Large Simulated and Limited Experimental Datasets

Guided wave-based structural health monitoring (GWSHM) with onboard transducers offers significant potential for the early diagnosis of damage in engineering structures. However, the practical deployment of deep learning models is often hindered by the limited availability of labelled experimental data and the high computational cost of generating large-scale high-fidelity simulation datasets.

fine-tuning

arXiv Machine Learning

Jul 7

Transformer-based Multisensor Data Fusion of Ultrasonic Guided Wave and FBG-based Strain Measurements for Multitask Aerospace Structural Health Monitoring

arXiv:2607. 02545v1 Announce Type: cross Abstract: Structural health monitoring (SHM) has emerged as an essential tool for ensuring the integrity and reliability of critical engineering structures, particularly in aerospace applications.

By Xin Yang, Morteza Moradi, Tongtong Yan, Jinbo Du, Yunlai Liao, Dimitrios Zarouchas, Dimitrios Chronopoulos

llms benchmarks

arXiv Machine Learning

Aug 10

Defining Energy Indicators for Impact Identification on Aerospace Composites: A Structured Feature Selection Approach Guided by Domain Knowledge

arXiv:2511. 01592v2 Announce Type: replace Abstract: Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface.

By Nat\'alia Ribeiro Marinho, Richard Loendersloot, Frank Grooteman, Jan Willem Wiegman, Uraz Odyurt, Tiedo Tinga

rag efficiency safety

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

Jun 24

An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data

arXiv:2606. 24459v1 Announce Type: new Abstract: Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environments.

By Jinghan Wang, Feng Cheng, Wentao Wu, Hang Li, Gaoliang Peng, Tianchen Liu

llms rag fine-tuning benchmarks safety

arXiv Machine Learning

Jun 16

Enhancing Physics-Informed Neural Networks Through Feature Engineering

arXiv:2502. 07209v4 Announce Type: replace Abstract: Physics-Informed Neural Networks (PINNs) seek to solve partial differential equations (PDEs) with deep learning.

By Shaghayegh Fazliani, Zachary Frangella, Madeleine Udell

arXiv AI

Aug 3

Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning

arXiv:2607. 28695v1 Announce Type: cross Abstract: Here is the plain text version optimized for arXiv's submission form.

By Aryuemaan Kumar Chowdhury

computer-vision benchmarks

arXiv AI

Aug 7

Spectral Aliasing Pretext: A novel task for Self-Supervised fault diagnosis in rotating machinery

arXiv:2608. 05705v1 Announce Type: cross Abstract: Deep learning is a new way for machinery fault diagnosis but requires extensive labeled data, a scarce resource in industrial settings.

By Victor Gialis, Maxime Metz, David Esteve, Abdenour Soualhi

llms fine-tuning

arXiv AI

Jun 12

Physics-Guided Spatiotemporal Learning for Coastal Wave Peak Period Estimation from Video

arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.

By Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Abhishek Srivastava, Paramasivam Saravanakumar

llms robotics fine-tuning benchmarks

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 AI

Jun 17

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.

By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni

safety

arXiv Machine Learning

Jun 19

RepNN: Tackling spectral bias in deep neural networks via parameter reparameterization

arXiv:2606. 16575v2 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in scientific computing, yet they often suffer from spectral bias in capturing oscillatory and multiscale behaviors.

By Yong Wang, Tao Zhou, Xuhui Meng

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