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Presented is an effective procedure for aerodynamic design of transonic wings, based on joint use of fast method for direct calculation and inverse and numerical optimization methods developed by the authors. Each component of the procedure developed is described. Some examples of designing sophisticated advanced configurations are given.

"Mechanism design is an analytical framework for thinking clearly and carefully about what exactly a given institution can achieve when the information necessary to make decisions is dispersed and privately held. This analysis provides an account of the underlying mathematics of mechanism design based on linear programming. Three advantages characterize the approach. The first is simplicity: arguments based on linear programming are both elementary and transparent. The second is unity: the machinery of linear programming provides a way to unify results from disparate areas of mechanism design. The third is reach: the technique offers the ability to solve problems that appear to be beyond solutions offered by traditional methods. No claim is made that the approach advocated should supplant traditional mathematical machinery. Rather, the approach represents an addition to the tools of the economic theorist who proposes to understand economic phenomena through the lens of mechanism design"--

Summary In a recent paper Snee and Marquardt [8] considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes.The purpose of this paper is to describe a numerical procedure resulting in a design of fixed size N, which is approximately D‐optimal, and where the components may be subject to linear constraints (f.e. upper or lower bounds). The proposed method is more generally applicable for models linear in the independent variables and the parameters and the convex hull of the experimental region is a polyhedron whose vertices are known.

IV Encuentro Oceanografía Física Española, celebrado del 20 al 22 de julio de 2016 en Alicante,España.-- 2 pages, 2 figures, 1 table ; The Brazil Current (BC) and the Malvinas Current (MC) meet at the Brazil-Malvinas Confluence (BMC) region. This encounter is characterized by a complex mesoscale structure, with numerous eddies, filaments and thermohaline intrusions that lead to meridional exchange of mass, heat and salt between both currents. Therefore, the BMC is the site where the subtropical gyre and the ACC exchange key properties, thus playing a fundamental role in the Earth's climate. During March 2015, the R/V Hespérides carried out the TIC-MOC cruise in the BMC with the objective of sampling the BMC with high resolution. Here we use 28 hydrographic sections from this cruise that, together with the South American coast, constitute a closed ocean volume with sides about 400 km, and build an inverse box model to quantify the mass exchange through its contour. We find that mass enters the region predominantly from the north, carried by the BC, and leave through the eastern section; most of the flow from the south also leaves the region through the southern boundary. The results are consistent with the velocity field as measured during the cruise ; This research has been supported by the Ministerio de Economía y Competitividad of the Spanish Government through projects TIC-MOC (CTM2011-28867) and VA-DE-RETRO (CTM2014-56987-P). Dorleta Orue-Echeverría has been funded through a FPU contract of the Ministerio de Educación y Ciencia ; Peer Reviewed

The problem of efficient design of material microstructures exhibiting desired properties spans a variety of engineering and science applications. An ability to rapidly generate microstructures that exhibit user-specified property distributions transforms the iterative process of traditional microstructure-sensitive design. We reformulate the microstructure design process as a constrained Generative Adversarial Network (GAN). This approach explicitly encodes invariance constraints within a GAN to generate two-phase morphologies for photovoltaic applications obeying design specifications: specifically, various short circuit current density and fill-factor combinations. Such invariance constraints can be represented by deep learning-based surrogates of full physics models mapping microstructure to photovoltaic properties. To circumvent data generation bottlenecks, we utilize a multi-fidelity surrogate that reduces the requirements of expensive labels by 5X. Our approach enables fast generation of microstructures (in~190ms) with user-defined properties. Such physics-aware data-driven methods for inverse design problems are expected to democratize and accelerate the field of microstructure-sensitive design.