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 100
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- low-carbon learning
- material modeling
- neural operator learning
- PDE-based learning
- multi-phase material
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.
