PDE-Based Neural Operator Learning for Material Modeling, Fatigue, and Fracture Mechanics
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 115
Special Issue Editors
Interests: material modeling; neural networks; structural health monitoring
Interests: structural integrity; fatigue; fracture mechanics; structure analysis; probabilistic models
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of machine learning (ML) into material modeling is transforming materials science. This Special Issue highlights the recent advancements in ML algorithms, with a focus on partial differential equation-based (PDE-based) neural operator learning, to improve material modeling across scales and support low-carbon learning pathways. By emphasizing physics-informed operator architectures for the accurate prediction of material behavior and fracture evolution, this Special Issue focuses on how computationally intensive data-driven training, such as in convolutional neural networks, can be made more sustainable by reducing computational costs.
PDE-based neural operators offer significant potential for advancing the prediction of complex phenomena, such as crack propagation and fatigue evolution, in multiphase materials like composites and concrete. These improvements enable the development of strategies for optimizing material use and reducing resource consumption.
This Special Issue invites contributions at the intersection of materials science and ML, with a particular focus on PDE-based operator learning for material modeling and fracture mechanics. These advancements aim to drive sustainable innovation in engineering and manufacturing, paving the way for environmentally responsible practices and technologies.
Dr. Natalie Rauter
Dr. José António Correia
Guest Editors
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Keywords
- low-carbon learning
- material modeling
- neural operator learning
- PDE-based learning
- multi-phase material
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