Intelligent Computing for Energy and Environment (ICEE) is an international, peer-reviewed, open-access journal published by Elamena Academic Press. ICEE publishes original research, reviews, and short communications that integrate intelligent computational methods with energy and environmental applications.
Aims
ICEE is dedicated to advancing computational methods that bridge data-driven and physics-based paradigms, and to publishing research with genuine real-world impact. The journal aims to:
- Advance intelligent computational methodologies — artificial intelligence, scientific machine learning, fractional calculus, and operator learning — for energy and environmental applications.
- Promote interdisciplinary research connecting computational science, applied mathematics, engineering, and sustainability.
- Encourage open, reproducible, and transparent research, including data and code sharing.
- Support global scientific exchange, diversity of authorship, and sustainable development.
- Maintain an ethical, COPE-compliant, and author-friendly editorial process.
Scope
ICEE welcomes original contributions across the following thematic areas (indicative, not exhaustive):
Intelligent Computing & Artificial Intelligence — machine learning, deep learning, and reinforcement learning; scientific machine learning (SciML); physics-informed neural networks (PINNs) and hybrid AI–physics models; neural operators (DeepONet, FNO); explainable and trustworthy AI (XAI); graph neural networks; generative AI and foundation models for science.
Advanced Computational Methods — finite element, finite volume, finite difference, lattice Boltzmann, and SPH methods; fractional calculus and fractional-order modelling (Caputo, Atangana–Baleanu); uncertainty quantification, polynomial chaos, and Bayesian inference; reduced-order and surrogate modelling; high-performance and multiphysics simulation.
Energy Systems — hydrogen technologies and underground hydrogen storage; carbon capture, utilisation and storage (CCUS); renewable energy, smart grids, and energy storage; subsurface and reservoir engineering.
Environmental Modelling — climate and atmospheric modelling; water resources and atmospheric water harvesting; pollution and dispersion modelling; circular economy and sustainable infrastructure.
Digital Technologies for Sustainability — digital twins, data-driven decision systems, and intelligent monitoring for energy and environmental applications.
