Petros Koumoutsakos

TL;DR: A novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES), intended to reduce the number of generations required for convergence to the optimum, which results in a highly parallel algorithm which scales favorably with large numbers of processors.

TL;DR: A computationally efficient, two-dimensional, feature point tracking algorithm for the automated detection and quantitative analysis of particle trajectories as recorded by video imaging in cell biology.

TL;DR: In this paper, a linear relationship between the contact angle and the water monomer binding energy on graphite was established and a new route to calibrate interaction potential parameters was presented, which was obtained by applying a carbon−oxygen Lennard-Jones potential with parameters eCO = 0.392 kJ mol-1 and σCO = 3.19 A.

TL;DR: An overview of machine learning for fluid mechanics can be found in this article, where the strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation.

TL;DR: The book Vortex Methods: Theory and Practice presents a comprehensive account of the numerical technique for solving fluid flow problems, and goes into some detail on the more recent developments that attempt to account for viscous effects.