International Conference on Mathematical Methods in Applied Physics (ICMMAP - 27)

This track focuses on innovative simulation methodologies that enhance the understanding of complex physical systems. Contributions that integrate computational models with experimental data are particularly encouraged.

This session invites papers that explore the application of statistical methods to quantum mechanical problems. Topics may include quantum state estimation, measurement theory, and statistical inference in quantum systems.

This track highlights the intersection of machine learning and computational physics, showcasing novel algorithms and their applications. Papers that demonstrate the effectiveness of machine learning in solving physical problems are welcome.

This session seeks contributions on optimization techniques tailored for various physical applications. Discussions may include algorithmic advancements and their implications for real-world problem-solving in physics.

This track emphasizes the development and application of numerical methods for solving differential equations in applied physics. Papers that address challenges in stability, convergence, and accuracy are encouraged.

This session focuses on the role of data science in assessing and managing risks within physical systems. Contributions that utilize statistical techniques and predictive modeling to inform risk management strategies are invited.