Advancements in Integrating Physics and Machine Learning for System Reliability, Risk, and Resilience Analysis
摘要截稿:
全文截稿: 2025-09-30
影响因子: 5.04
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 工程技术 - 1区
• 小类 : 工程:工业 - 1区
• 小类 : 运筹学与管理科学 - 1区
Overview
In recent years, machine learning's integration into reliability and safety analysis has surged, promising to promote the safety of complex systems like nuclear power and chemical plants and critical infrastructure. However, a persistent challenge remains including scarcity of labeled data on industrial anomalies and accidents, hampering broader machine learning adoption.To promote the adoption of machine learning, this special issue aims to highlight innovative research on machine learning focused on the advancement of reliability engineering and system safety. We seek contributions that leverage domain-specific knowledge, physics-based insights, and advanced models to overcome data limitations and enhance safety analysis.Key themes for this special issue include, but are not limited to:
Quantitative Models for System Reliability, Risk, and Resilience: Using physics-informed machine learning for predicting safety performance.
Physics-Informed Algorithms Development: Enhancing anomaly detection accuracy using physical laws.
Innovative Data Strategies: Employing synthetic data generation and transfer learning to augment real-world data.
Multi-Modal Data Fusion: Integrating diverse data sources for robust safety models.
Practical Applications in Industrial Safety: Demonstrating real-world implementations in emergency planning and predictive maintenance.
Contributions should strike a balance between theoretical depth and practical applicability, offering novel solutions for enhancing system safety of complex systems.
Guest editors:
Dr. Jihao Shi, Hong Kong Polytechnic University, Hong Kong, ChinaEmail: jihaoshi@polyu.edu.hk;
Dr. Ming Yang, Delft University of Technology, Delft, NetherlandsEmail: m.yang-1@tudelft.nl;
Dr. Maria Nogal, Delft University of Technology, Delft, NetherlandsEmail: m.nogal@tudelft.nl;
Prof. Nicola Paltrinieri, Norwegian University of Science and Technology, Trondheim, NorwayEmail: nicola.paltrinieri@ntnu.no.
Manuscript submission information:
Authors are invited to submit their papers to the Editorial Manager System of the journal: https://www2.cloud.editorialmanager.com/jress/default2.aspx
When submitting your manuscript please select the article type “VSI: Physics_ML”. Please submit your manuscript before the submission deadline.
The submission portal will open by 15-March-2024;
The submission deadline is 30-April-2025;
The acceptance deadline is 30-September-2025.
All submissions deemed suitable to be sent for peer review will be reviewed by at least two independent reviewers. Once your manuscript is accepted, it will go into production, and will be simultaneously published in the current regular issue and pulled into the online Special Issue. Articles from this Special Issue will appear in different regular issues of the journal, though they will be clearly marked and branded as Special Issue articles.
Please ensure you read the Guide for Authors before writing your manuscript. The Guide for Authors and the link to submit your manuscript is available on the Journal’s homepage.
Learn more about the benefits of publishing in a special issue.
Interested in becoming a guest editor? Discover the benefits of guest editing a special issue and the valuable contribution that you can make to your field.
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In recent years, machine learning's integration into reliability and safety analysis has surged, promising to promote the safety of complex systems like nuclear power and chemical plants and critical infrastructure. However, a persistent challenge remains including scarcity of labeled data on industrial anomalies and accidents, hampering broader machine learning adoption.To promote the adoption of machine learning, this special issue aims to highlight innovative research on machine learning focused on the advancement of reliability engineering and system safety. We seek contributions that leverage domain-specific knowledge, physics-based insights, and advanced models to overcome data limitations and enhance safety analysis.Key themes for this special issue include, but are not limited to:
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Contributions should strike a balance between theoretical depth and practical applicability, offering novel solutions for enhancing system safety of complex systems.
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Guest editors:
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Dr. Jihao Shi, Hong Kong Polytechnic University, Hong Kong, ChinaEmail: jihaoshi@polyu.edu.hk;
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Dr. Ming Yang, Delft University of Technology, Delft, NetherlandsEmail: m.yang-1@tudelft.nl;
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Dr. Maria Nogal, Delft University of Technology, Delft, NetherlandsEmail: m.nogal@tudelft.nl;
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Prof. Nicola Paltrinieri, Norwegian University of Science and Technology, Trondheim, NorwayEmail: nicola.paltrinieri@ntnu.no.