Neural Operators and Physics-Informed Neural Networks: Theory and Applications in Complex Systems
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Complexity".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 379
Editors
Interests: fluid dynamics; energy materials; physics-informed ML; data-driven modeling; ML-driven optimization
Interests: atomistic modeling; data-driven modeling and optimization; energy materials; materials modeling; model reduction; dynamical systems
Special Issues, Collections and Topics in MDPI journals
Interests: numerical analysis; scientific machine learning; nonlinear dynamics; complex systems; data mining
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue focuses on recent advances in neural operators and physics-informed neural networks (PINNs) for modeling and numerical analysis of multiscale dynamical systems and complex systems. Neural operators provide a powerful framework for learning solution operators and right-hand-sides of partial differential equations and complex systems directly in function space, while PINNs incorporate physical laws to enhance data efficiency, interpretability, and generalization. Together, these approaches offer new perspectives for learning physical laws and good collective variables from data, prediction, and control of complex dynamical phenomena. The issue welcomes contributions on theoretical developments, numerical analysis, and practical applications, including but not limited to complex and multi-scale complex systems, long-term forecasting of high-dimensional time series, uncertainty quantification, and data-driven discovery of governing equations across science and engineering domains.
Prof. Dr. Luca Bergamasco
Prof. Dr. Eliodoro Chiavazzo
Prof. Dr. Constantinos Siettos
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 250 words) can be sent to the Editorial Office for assessment.
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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly 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 2600 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
- neural operators
- physics-informed neural networks (PINNs)
- multiscale dynamical systems
- complex systems
- high-dimensional time series forecasting
- partial differential equations (PDEs)
- data-driven modeling
- learning physical laws
- learning collective variables
- uncertainty quantification
- numerical analysis
- scientific machine learning
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.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
