This paper presents a novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to reduce the effect of noise; (2) to improve global search properties; and (3) to implement the algorithm on (highly) parallel machines. Our method results in a highly parallel algorithm which scales favorably with large numbers of processors. This is accomplished by efficiently incorporating the available information from a large population, thus significantly reducing the number of generations needed to adapt the covariance matrix. The original version of the CMA-ES was designed to reliably adapt the covariance matrix in small populations but it cannot exploit large populations efficiently. Our modifications scale up the efficiency to population sizes of up to 10n, where n is the problem dimension. This method has been applied to a large number of test problems, demonstrating that in many cases the CMA-ES can be advanced from quadratic to linear time complexity.

No abstract

A systematic molecular dynamics study shows that the contact angle of a water droplet on graphite changes significantly as a function of the water−carbon interaction energy. Together with the observation that a linear relationship can be established between the contact angle and the water monomer binding energy on graphite, a new route to calibrate interaction potential parameters is presented. Through a variation of the droplet size in the range from 1000 to 17 500 water molecules, we determine the line tension to be positive and on the order of 2 × 10-10 J/m. To recover a macroscopic contact angle of 86°, a water monomer binding energy of −6.33 kJ mol-1 is required, which is obtained by applying a carbon−oxygen Lennard-Jones potential with the parameters εCO = 0.392 kJ mol-1 and σCO = 3.19 Å. For this new water−carbon interaction potential, we present density profiles and hydrogen bond distributions for a water droplet on graphite.

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning presents us with a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. We outline fundamental machine learning methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that links data with modeling, experiments, and simulations. Machine learning provides a powerful information processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

Cell migration plays a major role in development, physiology, and disease, and is frequently evaluated in vitro by the monolayer wound healing assay. The assay analysis, however, is a time-consuming task that is often performed manually. In order to accelerate this analysis, we have developed TScratch, a new, freely available image analysis technique and associated software tool that uses the fast discrete curvelet transform to automate the measurement of the area occupied by cells in the images. This tool helps to significantly reduce the time needed for analysis and enables objective and reproducible quantification of assays. The software also offers a graphical user interface which allows easy inspection of analysis results and, if desired, manual modification of analysis parameters. The automated analysis was validated by comparing its results with manual-analysis results for a range of different cell lines. The comparisons demonstrate a close agreement for the vast majority of images that were examined and indicate that the present computational tool can reproduce statistically significant results in experiments with well-known cell migration inhibitors and enhancers.