Isogeometric analysis (IGA) has been a particularly impactful development in the realm of Kirchhoff-Love thin-shell analysis because the high-order basis functions employed naturally satisfy the requirement of C 1 continuity. Still, engineering models of appreciable complexity, such as wind turbine blades, are typically modeled using multiple surface patches and, often, neither rotational continuity nor conforming discretization can be practically obtained at patch interfaces. A penalty approach for coupling adjacent patches is therefore presented. The proposed method imposes both displacement and rotational continuity and is applicable to either smooth or nonsmooth interfaces and either matching or non-matching discretization. The penalty formulations require only a single, dimensionless penalty coefficient for both displacement and rotation coupling terms, alleviating the problem-dependent nature of the penalty parameters. Using this coupling methodology, numerous benchmark problems encapsulating a variety of analysis types, geometrical and material properties, and matching and non-matching interfaces are addressed. The coupling methodology produces consistently accurate results throughout all tests. Furthermore, the suggested penalty coefficient of α = 10 3 is shown to be effective for the wide range of problem configurations addressed. Finally, a realistic wind turbine blade model, consisting of 27 patches and 51 coupling interfaces and having a chordwise-and spanwise-variant composite material definition, is subjected to buckling, vibration, and nonlinear deformation analysis using the proposed approach.

We present a new workflow for differentially-private publication of graph topologies. First, we produce differentiallyprivate measurements of interesting graph statistics using our new version of the PINQ programming language, Weighted PINQ, which is based on a generalization of differential privacy to weighted sets. Next, we show how to generate graphs that fit any set of measured graph statistics, even if they are inconsistent (due to noise), or if they are only indirectly related to actual statistics that we want our synthetic graph to preserve. We combine the answers to Weighted PINQ queries with an incremental evaluator (Markov Chain Monte Carlo (MCMC)) to synthesize graphs where the statistic of interest aligns with that of the protected graph. This paper presents our preliminary results; we show how to cast a few graph statistics (degree distribution, edge multiplicity, joint degree distribution) as queries in Weighted PINQ, and then present experimental results synthesizing graphs generated from answers to these queries.

We present an approach to differentially private computation in which one does not scale up the magnitude of noise for challenging queries, but rather scales down the contributions of challenging records. While scaling down all records uniformly is equivalent to scaling up the noise magnitude, we show that scaling records non-uniformly can result in substantially higher accuracy by bypassing the worst-case requirements of differential privacy for the noise magnitudes. This paper details the data analysis platform wPINQ , which generalizes the Privacy Integrated Query (PINQ) to weighted datasets. Using a few simple operators (including a non-uniformly scaling Join operator) wPINQ can reproduce (and improve) several recent results on graph analysis and introduce new generalizations ( e.g. , counting triangles with given degrees). We also show how to integrate probabilistic inference techniques to synthesize datasets respecting more complicated (and less easily interpreted) measurements.

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