arXiv Machine Learning

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.

arXiv Machine Learning

Jun 26

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.

By Santosh Kapuria, Abhishek

fine-tuning

arXiv Machine Learning

6d ago

Latent variable models for simultaneous EOV identification and removal in population-based SHM

arXiv:2608. 11995v1 Announce Type: cross Abstract: The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM).

By M. D. Champneys, M. R. Jones, A. J. Hughes, T. J. Rogers, E. J. Cross, K. Worden

benchmarks

arXiv AI

Aug 11

Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields

arXiv:2608. 09071v1 Announce Type: cross Abstract: Forward uncertainty propagation in complex physical systems can induce structured covariance across field-valued outputs.

By Yupei Nie, Lei Wang, Jiasen Liu

Hugging Face Trending Papers

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

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

Jun 19

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

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.

By Bilal Ahmed, Diab W. Abueidda, Waleed El-Sekelly, Tarek Abdoun, Mostafa E. Mobasher

benchmarks

arXiv Machine Learning

Jul 3

eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions

arXiv:2607. 01275v1 Announce Type: cross Abstract: Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture the true distribution of latent representations for complex datasets.

By Qijun Chen, Shaofan Li

diffusion benchmarks

arXiv Machine Learning

Jun 5

Function-Space Priors for Bayesian Neural ODEs with Application to Vessel Trajectory Prediction

arXiv:2606. 06351v1 Announce Type: cross Abstract: Vessel trajectory prediction from Automatic Identification System (AIS) data is essential for maritime situational awareness, yet it remains challenging due to irregular sampling, missing reports, and complex dynamics.

By Jaeyeong Lee, Wonmo Koo, Heeyoung Kim

arXiv Machine Learning

Aug 7

Dual-space posterior sampling for Bayesian inference in constrained inverse problems

arXiv:2603. 00393v2 Announce Type: replace-cross Abstract: Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy, incomplete data or inherent non-uniqueness.

By Ali Siahkoohi, Kamal Aghazade, Ali Gholami

benchmarks

arXiv AI

Jun 9

Developing Distance-Aware Physics-Constrained Probabilistic Frameworks for Industrial Prognostics

arXiv:2512. 08499v3 Announce Type: replace-cross Abstract: Development of reliable and physically interpretable probabilistic frameworks for industrial prognostics remain nascent, and existing literature is often insensitive as inputs move away from the training manifold.

By Waleed Razzaq, Yun-Bo Zhao

diffusion benchmarks safety

arXiv Machine Learning

1d ago

Identifying parameter couplings and uncertainties of mixed-noise stochastic systems via full-covariance Gaussian mixture network

arXiv:2608. 15198v1 Announce Type: cross Abstract: Parameter identification of stochastic dynamical systems driven by mixed noises is challenging due to intractable likelihood functions.

By Xiaolong Wang, Xiangwen Hao, Jing Feng, Yuanyuan Liu, Yong Xu

multimodal

arXiv AI

Jun 2

Physically-Constrained Mamba-SDE for Remaining Useful Life Prediction under Irregular Observations

arXiv:2606. 01894v1 Announce Type: new Abstract: Accurate Remaining Useful Life prediction is critical for industrial predictive maintenance.

By Deyu Zhuang, Peiliang Gong, Yang Shao, Liyuan Shu, Qi Zhu, Xiaoli Li, Daoqiang Zhang

rag benchmarks safety

Related stories