Transactions on Computational and Applied Mathematics (TCAM)
Transactions on Computational and Applied Mathematics (TCAM) is a journal dedicated to promoting and accelerating the dissemination of new research findings. There is a wealth of exciting research activity in this field worldwide. The journal aims to provide academicians and scientists around the globe with a platform to share, promote, and discuss various emerging issues and developments in different areas of computational and applied mathematics.
Aims & Scope
Transactions on Computational and Applied Mathematics (TCAM) is a journal dedicated to promoting and accelerating the dissemination of new research findings. There is a wealth of exciting research activity in this field worldwide. The journal aims to provide academicians and scientists around the globe with a platform to share, promote, and discuss various emerging issues and developments in different areas of computational and applied mathematics.
Aims
- To promote and accelerate the dissemination of new research findings across the fields of computational and applied mathematics globally.
- To provide academicians and scientists worldwide with a platform to share, promote, and discuss emerging issues and developments in all areas of computational and applied mathematics.
- To foster academic exchange, collaboration, and innovation in interdisciplinary research addressing the evolving challenges of modern computational and applied mathematics.
- To advance knowledge and practical solutions for real-world problems through rigorous scholarly publication, bridging mathematical theory with computational practice.
Scope
The journal covers a wide range of topics related to computational and applied mathematics, including but not limited to:
Computational Mathematics and Algorithms
- Numerical analysis and methods, including finite difference, finite element, and spectral methods for solving differential equations
- Optimization and numerical linear algebra, including iterative methods, eigenvalue problems, and convex optimization algorithms
- Scientific computing and high-performance computing, including parallel algorithms, GPU computing, and large-scale simulations
- Stochastic and probabilistic computing, including Monte Carlo methods, Bayesian computation, and stochastic differential equations
Applied Mathematics and Modeling
- Mathematical modeling in engineering and sciences, including continuum mechanics, fluid dynamics, and mathematical physics
- Differential equations and dynamical systems, including ordinary/partial differential equations, stability analysis, and chaos theory
- Mathematical biology and computational modeling, including population dynamics, ecological modeling, and systems biology
- Mathematical finance and risk analysis, including stochastic models, financial engineering, and quantitative risk management