Interdisciplinary Modeling and Intelligent approaches in Natural Sciences
Keywords:
Abstract
The Journal of SciMeTech is an interdisciplinary, peer-reviewed publication dedicated to advancing research across the full spectrum of natural sciences.
With a special focus on the integration of computational science, artificial intelligence, and programming, the journal provides a platform for innovative studies that address complex scientific challenges through modern technologies.
We welcome original research, reviews, and methodological contributions in fields such as physics, chemistry, environmental science, medical physics, biophysics, and geosciences. The journal actively encourages submissions that apply simulation, modeling, machine learning, and open-source programming to natural phenomena.
Our goal is to foster a dynamic and collaborative scientific community where high-impact discoveries, reproducible methods, and digital innovation converge to redefine the future of natural science research.
Key Natural Science Domains & Computational Approaches
II. Physical Sciences
Advances knowledge of matter, energy, and physical laws with emphasis on computational applications.
Subfields:
Computational Methods:
Tools:
III. Environmental & Earth Sciences
Explores dynamic processes shaping our planet, focusing on Earth systems, climate, and human-environment interactions.
Subfields:
AI Applications:
Tools:
IV. Interdisciplinary Natural Science & AI
Bridges disciplines showcasing AI, ML, and computational modeling in solving complex scientific problems.
Key Areas:
Advanced AI Methods:
Applications: Population modeling, ecosystem simulations, agent-based modeling, cross-domain knowledge linking
V. Software, Code & Data Tools in Natural Sciences
A specialized section dedicated to the development of scientific software, custom algorithms, data platforms, and open-access tools.
Focus Areas:
Journal Emphasis
Special interest in work promoting reproducibility, open science, and development of scientific tools. Encourages submissions with novel algorithms, analytical workflows, or computational pipelines.
Computational & AI Integration
This journal places particular emphasis on computational methods, artificial intelligence, and programming as essential tools for 21st-century science. We encourage contributions that leverage machine learning, simulation, modeling, and data-driven methodologies to investigate natural phenomena.
Whether it's using neural networks for disease diagnosis, deep learning to analyze satellite imagery, or Python scripts to simulate quantum systems, we value research that blends theory, experimentation, and code. We recognize the importance of open-source programming, scientific computing libraries, and high-performance computing in accelerating discovery.
How to cite this paper:
Rachid BENBRIK, "Interdisciplinary Modeling and Intelligent approaches in Natural Sciences", Sciences Methods and Technologies International Journal (SciMeTech), Vol 1, Issue 1, p16-18