Machine Learning and Numerical Modeling in Sustainable Energy and Environmental Applications
A Special Issue of Environments (ISSN 2076-3298) belonging to the section "Environmental Monitoring and Management".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 313
Editors
Interests: renewable energy; remote sensing; applied numerical methods for the environment; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid evolution of computational modeling and artificial intelligence has enabled significant advancements in solving complex challenges in sustainable energy and environmental sciences. Integrating machine learning, deep learning, and advanced numerical simulations fosters more accurate predictions, improved system optimization, and robust data-driven solutions for real-world sustainability problems.
In this context, the Special Issue “Machine Learning and Numerical Modeling in Sustainable Energy and Environmental Applications” seeks to provide a comprehensive forum for innovative research that bridges computational intelligence and numerical approaches. We aim to collect high-quality contributions that develop novel algorithms/architectures, hybrid modeling frameworks, rigorous assessment of numerical methods, and practical applications that help advance sustainable energy systems and environmental modeling/forecasting.
This Special Issue welcomes original research articles, reviews, methodological developments, case studies, and benchmarking analyses that address, but are not limited to, the following topics:
- Machine learning and deep learning models for energy and environment applications;
- Hybrid approaches combining artificial intelligence with numerical simulations and physics-based models;
- Computational Fluid Dynamics (CFD) and multi-physics models coupled with data-driven methods;
- Remote sensing applications for environmental monitoring and renewable energy assessment;
- Satellite and ground-based data integration using machine learning and numerical modeling;
- Solar, wind, biomass and other renewable energies forecasting, resource assessment, and optimization;
- Green hydrogen systems, smart grid integration, and sustainable process design;
- Air and water quantity and/or quality prediction, environmental risk assessment, and climate-related modeling;
- Uncertainty quantification, interpretability, and transferability in AI-enhanced models.
We invite authors to share their latest findings, insights, and best practices in developing intelligent and reliable computational strategies that contribute to sustainable energy solutions and environmental resilience. Manuscripts addressing methodological rigor, practical deployments, comparative evaluations, and real-world case studies are particularly encouraged.
Prof. Dr. Paulo Rocha
Prof. Dr. Matheus Pereira Porto
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. Environments 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 1800 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
- machine learning
- numerical modeling
- sustainable energy
- environmental forecasting
- remote sensing
- hybrid modeling
- CFD
- deep learning
- renewable energy systems and applications
- green hydrogen
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