Cover Image

Green Physics-Informed Machine Learning Models For Structural Health Monitoring

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bradley, Daisy R, Cross, Elizabeth J
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Canonical-Correlation-Based Fast Feature Selection for Structural Health Monitoring
by: Zhang, Sikai, et al.
Published: (2021)

Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification
by: Aizpurua, Jose I.
Published: (2025)

A Machine Learning-Driven Wireless System for Structural Health Monitoring
by: Pop, Marius, et al.
Published: (2024)

Physics-Informed Machine Learning for Characterizing System Stability
by: Koike, Tomoki, et al.
Published: (2025)

When Plants Respond: Electrophysiology and Machine Learning for Green Monitoring Systems
by: Buss, Eduard, et al.
Published: (2025)

Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part I: Basic Concepts, Neural Networks, and Variants
by: Aizpurua, Jose I.
Published: (2025)

Computational, Data-Driven, and Physics-Informed Machine Learning Approaches for Microstructure Modeling in Metal Additive Manufacturing
by: Patel, D., et al.
Published: (2025)

Towards Sustainable Development: A Novel Integrated Machine Learning Model for Holistic Environmental Health Monitoring
by: Mazumder, Anirudh, et al.
Published: (2023)

A Digital Twin for Diesel Engines: Operator-infused Physics-Informed Neural Networks with Transfer Learning for Engine Health Monitoring
by: Nath, Kamaljyoti, et al.
Published: (2024)

Uncertainty-Aware Diagnostics for Physics-Informed Machine Learning
by: Daniels, Mara, et al.
Published: (2025)

On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
by: Lindley, C. A., et al.
Published: (2025)

Physics-Informed Machine Learning for Seismic Response Prediction OF Nonlinear Steel Moment Resisting Frame Structures
by: Bond, R. Bailey, et al.
Published: (2024)

Model Based and Physics Informed Deep Learning Neural Network Structures
by: Mohammad-Djafari, Ali, et al.
Published: (2024)

A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection
by: Wu, Yuandi, et al.
Published: (2024)

Time-Vertex Machine Learning for Optimal Sensor Placement in Temporal Graph Signals: Applications in Structural Health Monitoring
by: Niresi, Keivan Faghih, et al.
Published: (2025)

Physics-Informed Reward Machines
by: Ajeleye, Daniel, et al.
Published: (2025)

Exploring Efficient Quantification of Modeling Uncertainties with Differentiable Physics-Informed Machine Learning Architectures
by: Oddiraju, Manaswin, et al.
Published: (2025)

Physics-Informed Extreme Learning Machine (PIELM): Opportunities and Challenges
by: Yang, He, et al.
Published: (2025)

Physics-Informed Machine Learning On Polar Ice: A Survey
by: Liu, Zesheng, et al.
Published: (2024)

On-Device Crack Segmentation for Edge Structural Health Monitoring
by: Zhang, Yuxuan, et al.
Published: (2025)

A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
by: Nguyen, Thien V., et al.
Published: (2026)

A Rapid Physics-Informed Machine Learning Framework Based on Extreme Learning Machine for Inverse Stefan Problems
by: Zhuang, Pei-Zhi, et al.
Published: (2025)

Machine Learning for Health symposium 2024 -- Findings track
by: Hegselmann, Stefan, et al.
Published: (2025)

Learning to Learn the Macroscopic Fundamental Diagram using Physics-Informed and meta Machine Learning techniques
by: Roark, Amalie, et al.
Published: (2025)

NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
by: Sharpe, Peter, et al.
Published: (2025)

Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
by: Mandl, Luis, et al.
Published: (2024)

Reliable Thermal Monitoring of Electric Machines through Machine Learning
by: Kakosimos, Panagiotis
Published: (2025)

Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
by: Farea, Afrah, et al.
Published: (2025)

Learning Biomolecular Motion: The Physics-Informed Machine Learning Paradigm
by: Deshpande, Aaryesh
Published: (2025)

Physics-Informed Machine Learning for Pouch Cell Temperature Estimation
by: Liu, Zheng
Published: (2026)

Physically-informed change-point kernels for structural dynamics
by: Pitchforth, Daniel James, et al.
Published: (2025)

Informed Machine Learning with Knowledge Landmarks
by: Dai, Chuyi, et al.
Published: (2026)

Probabilities-Informed Machine Learning
by: Rashki, Mohsen
Published: (2024)

Training Variation of Physically-Informed Deep Learning Models
by: Lenau, Ashley, et al.
Published: (2025)

The Physics Constraint Paradox: When Removing Explicit Constraints Improves Physics-Informed Data for Machine Learning
by: Ray, Rahul D
Published: (2025)

Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes
by: Metwally, Ahmed A., et al.
Published: (2025)

A Physics Informed Machine Learning Framework for Optimal Sensor Placement and Parameter Estimation
by: Venianakis, Georgios, et al.
Published: (2025)

A Critical Review of Physics-Informed Machine Learning Applications in Subsurface Energy Systems
by: Latrach, Abdeldjalil, et al.
Published: (2023)

Parameter Identification for Partial Differential Equation with Jump Discontinuities in Coefficients by Markov Switching Model and Physics-Informed Machine Learning
by: Zhang, Zhikun, et al.
Published: (2025)

Physics-Informed Machine Learning in Biomedical Science and Engineering
by: Ahmadi, Nazanin, et al.
Published: (2025)