Advanced Modeling and Simulation in Engineering Sciences is a fully open access journal focusing on advanced modeling and simulation of materials, processes, and structures.
Digital twins promise real-time, continuously updated predictions of engineered systems, which calls for a predictive core that is fast, updatable from data, and accurate across varying physical parameters, loading and geometry. A natural route to such a core is to learn the operator of the underlying mechanics; i.e., the map from a problem's input (e.g., material properties, loads, boundary and initial conditions, geometry) to its response. Neural operators, which learn maps directly between function spaces, have emerged as a powerful tool for this, and the field is advancing quickly.
This Topical Collection focuses on operator learning as a methodology, motivated by the needs of engineering digital twins. It welcomes any contribution to learning mechanics operators in this context: from neural operator architectures and their theory to training strategies, uncertainty quantification, model updating and applications. Contributions span the whole of mechanics and multiphysics. Submissions addressing methodological developments and their demonstration on engineering-relevant cases are especially encouraged.
Submissions aligned with the following topics are expected:
- Operator learning and neural operators for parametric PDEs in mechanics and multiphysics
- Operator-based surrogates for real-time data assimilation, model updating and prediction
- Uncertainty quantification, reliability and verification of learned operators
- Hybrid physics-informed and data-driven operator learning
- Operator learning across complex geometries and transfer across regimes and configurations
- Integrating learned operators into digital-twin workflows: sensing, monitoring and simulation-based decision-making
Submission guidelines
All papers must be prepared in accordance with the Submission Guidelines. Articles for this Topical Collection should be submitted via our submission system,SNAPP. During the submission process you will be asked whether you are submitting to a Collection, please select "Operator Learning and Neural Operators for Digital Twins in Engineering" from the dropdown menu.
Submitted papers should present original, unpublished work, relevant to one of the topics of the Topical Collection. All papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation by at least two reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Final decisions on all papers are made by the Editor-in-Chief.
Submit your manuscript to this collection through the participating journal.
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Robin Bouclier PhD
Clément Ader Institute (ICA), INSA Toulouse, France.Robin Bouclier is Full Professor at INSA Toulouse, conducting his research at the Clément Ader Institute (ICA) and as an associate researcher at the Institute of Mathematics of Toulouse (IMT). He is also a junior fellow of the Institut Universitaire de France (IUF). He develops advanced computational methods mainly for solid mechanics, ranging from efficient forward simulation to inverse problems and data assimilation. A growing part of his work targets digital twins: he led the French ANR project AVATAR, building image-based digital twins of architected materials, and co-leads a chair within ANITI (the Artificial and Natural Intelligence Toulouse Institute) on physics-informed learning. His work is recognised within the CSMA community (the French Computational Structural Mechanics Association), which notably invited him to deliver a plenary lecture at its 2026 congress.
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Eleni Chatzi PhD
ETH Zurich, Zurich, Switzerland.Eleni Chatzi is Full Professor and Chair of Structural Mechanics and Monitoring at ETH Zürich. Her work bridges advanced simulation, sensing, and hybrid methods, including physics-enhanced machine learning, to monitor, update, and manage engineered systems. She has led major initiatives in structural health monitoring and digital twinning and currently serves as Chair of SWICCOMAS, having previously served as President of the European Academy of Wind Energy (EAWE). Her research has been recognised with distinctions including an ERC Starting Grant, the ASCE Walter L. Huber Civil Engineering Research Prize, and the 2024 SHM Person of the Year award.
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Stefania Fresca PhD
University of Washington, United States.Stefania Fresca is Assistant Professor in Physics-based AI at the Department of Mechanical Engineering, University of Washington, and Visiting Assistant Professor at the Department of Computer Science, University of Cambridge. She earned her Ph.D. in “Mathematical Models and Methods in Engineering” at Politecnico di Milano, in the framework of the ERC Advanced Grant Project iHEART led by Prof. Alfio Quarteroni and devoted to cardiac modeling. After obtaining her Ph.D. Degree cum laude in 2021, she was recognized the Runner-up Best Ph.D. Award in Biomedical Engineering at CMBE22. Her research interests and expertise include scientific machine learning, reduced order modeling, digital twins, structure-preserving neural networks, multiscale deep learning and numerical approximation of PDEs, with several applications to engineering problems.
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George Karniadakis PhD
Brown University, United States.George Em Karniadakis is the Charles Pitts Robinson and John Palmer Barstow Professor of Applied Mathematics and Engineering at Brown University, and a member of the U.S. National Academy of Engineering. He is a pioneer of scientific machine learning, to whom we owe, in particular, physics-informed neural networks (PINNs) and deep operator networks (DeepONet). His recent work develops neural operators as foundation models for engineering digital twins. He is a Fellow of SIAM, APS, ASME and AAAS, and his many honors include the SIAM/ACM Prize in Computational Science and Engineering, the SIAM Ralph E. Kleinman Award and the inaugural J. Tinsley Oden Medal.
