Applied Probability and Uncertainty Quantification Research Researchers in Applied Probability and Uncertainty Quantification within the KAUST AMCS Program focus on stochastic modeling, Bayesian inference, and uncertainty quantification for complex systems, with applications in computational fluid mechanics, data assimilation, and risk assessment.
Mathematical Modeling Research Researchers in this area focus on integrating mathematical modeling with advanced computational techniques, with applications in wave propagation, complex media, multiscale phenomena, computational medicine, and computational fluid dynamics.
Mathematics of Data Science, Machine Learning and Optimization Research Researchers in the mathematics of data science, machine learning, and optimization focus on algorithmic complexity, combinatorial optimization, and machine learning applications, including deep learning for partial differential equations and discrete optimization.
Numerical Analysis and High-Performance Computing Research Researchers in numerical analysis and high-performance computing focus on scalable solvers for partial differential equations, data sparsity techniques, numerical methods for differential equations, algorithms for compressible flows, and parallel computing implementations.
Partial Differential Equations and Applied Analysis Research Researchers in partial differential equations (PDEs) and applied analysis focus on the theoretical foundations and applications of PDEs in physics, biology, economics, and engineering. Their research includes mean-field games, Hamilton-Jacobi equations, nonlinear PDEs, free boundary problems, the calculus of variations, and regularity theory.
Research Areas Research Research at KAUST AMCS program combines theory, modeling and computation. Shaheen III and Ibex empower impactful research across five core areas.
