Scientific Computing and Machine Learning in Engineering
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E: Applied Mathematics".
Deadline for manuscript submissions: 31 January 2027 | Viewed by 132
Special Issue Editors
Interests: lattice element method; coupled multiphysics damage interactions; land & marine geomechanics and geotechnics; material design with bio and nano-fillers; machine learning/deep learning; numerical modelling (bem/fem); buried energy transportation & storage systems
Interests: computational methods; computational mechanics; materials engineering; mechanics; rock mechanics; geomechanics; discrete element method; soil mechanics; civil engineering; geotechnics; localized failure; lattice element model; lattice element method
Special Issue Information
Dear Colleagues,
Engineering systems increasingly exhibit nonlinear, multiscale, and tightly coupled behaviour that pushes conventional analysis to its limits. Fracture and damage evolution in geomaterials and concrete, thermo-hydro-mechanical processes in porous media, and optimization of hybrid renewable energy infrastructures all demand models that are both physically faithful and computationally tractable. High-fidelity scientific computing provides this fidelity, yet often at a cost that restricts parametric studies, uncertainty quantification, and real-time decision support. In parallel, machine learning delivers fast surrogates and pattern discovery, but may suffer from poor extrapolation, limited interpretability, and violation of governing laws when used in isolation.
This Special Issue invites contributions that unite scientific computing and machine learning through mathematically consistent formulations and scalable implementations. Topics include finite element, boundary element, discrete and lattice methods; reduced-order modelling; operator learning; physics-informed and physics-constrained neural networks; data assimilation; reinforcement learning for design and control; and robust verification, validation, and uncertainty quantification. We particularly encourage papers that report algorithmic innovations, evidence of stability and convergence, and high-performance computing strategies (CPU/GPU parallelization).
By bridging equation-based modelling with information-based inference, the Special Issue aims to advance predictive engineering for resilient infrastructure, sustainable energy systems, and environmental risk mitigation. Submissions ranging from theory to demonstrators are welcome.
Dr. Zarghaam Haider Rizvi
Dr. Mijo Nikolić
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics 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 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
- scientific computing
- multiphysics coupling
- physics-informed neural networks
- lattice and discrete element methods
- machine learning
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