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  • Optimizing shapes and structures for freeform architecture

    Jiang, Caigui; Pottmann, Helmut (Submitted to Baustatik-Baupraxis 14, University of Stuttgart, 2020) [Conference Paper]
    Digital design and fabrication of complex geometries in architecture stimulated a significant amount of research most of which involves optimization. We present the main optimization methods with a focus on our preferred techniques. There, we discuss initialization, the formulation of constraints and their simplification through geometric considerations, and key concepts for regularization. This is illustrated at hand of a very recent type of discrete surfaces which are expressed via the pair of diagonal meshes in a quad mesh.
  • Geometry and Statics of Optimal Freeform Gridshells

    PELLIS, DAVIDE; Pottmann, Helmut (Submitted to Baustatik-Baupraxis 14, University of Stuttgart, 2020) [Conference Paper]
    We present our recent results on the geometric and static optimization of freeform load-bearing architectural skins. An efficient design strategy is the use of planar cladding panels supported by a prismatic framework substructure with optimized static performance. We show how these structures can be achieved discretizing membranes where principal stress and curvature directions coincide, and the absolute principal stresses are minimized. We outline then a design workflow and we provide some architectural examples.
  • Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.

    Smaili, Fatima Z.; Gao, Xin; Hoehndorf, Robert (Bioinformatics (Oxford, England), Oxford University Press (OUP), 2019-12-11) [Article]
    Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. The domain knowledge in biomedical ontologies may also have the potential to provide background knowledge for machine learning and predictive modelling. We use ontology-based machine learning methods to evaluate the contribution of formal axioms and ontology meta-data to the prediction of protein-protein interactions and gene-disease associations. We find that the background knowledge provided by the Gene Ontology and other ontologies significantly improves the performance of ontology-based prediction models through provision of domain-specific background knowledge. Furthermore, we find that the labels, synonyms and definitions in ontologies can also provide background knowledge that may be exploited for prediction. The axioms and meta-data of different ontologies contribute to improving data analysis in a context-specific manner. Our results have implications on the further development of formal knowledge bases and ontologies in the life sciences, in particular as machine learning methods are more frequently being applied. Our findings motivate the need for further development, and the systematic, application-driven evaluation and improvement, of formal axioms in ontologies.
  • A hierarchical bi-resolution spatial skew-t model

    Tagle, Felipe; Castruccio, Stefano; Genton, Marc G. (Spatial Statistics, Elsevier BV, 2019-12-09) [Article]
    Advances in Gaussian methodology for spatio-temporal data have made it possible to develop sophisticated non-stationary models for very large data sets. The literature on non-Gaussian spatio-temporal models is comparably sparser and strongly focused on distributing the uncertainty across layers of a hierarchical model. This choice allows to model the data conditionally, to transfer the dependence structure at the process level via a link function, and to use the familiar Gaussian framework. Conditional modeling, however, implies an (unconditional) distribution function that can only be obtained through integration of the latent process, with a closed form only in special cases. In this work, we present a spatio-temporal non-Gaussian model that assumes an (unconditional) skew- data distribution, but also allows for a hierarchical representation by defining the model as the sum of a small and a large scale spatial latent effect. We provide semi-closed form expressions for the steps of the Expectation-Maximization algorithm for inference, as well as the conditional distribution for spatial prediction. We demonstrate how it outperforms a Gaussian model in a simulation study, and show an example of application to precipitation data in Colorado.
  • A Particle Filter-based Adaptive Inflation Scheme for the Ensemble Kalman Filter

    Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim (Quarterly Journal of the Royal Meteorological Society, Wiley, 2019-12-09) [Article]
    An adaptive covariance inflation scheme is proposed for the ensemble Kalman filter (EnKF) to mitigate for the loss of ensemble variance. Adaptive inflation methods are mostly based on a Bayesian approach, which considers the inflation factor as a random variable with a given prior probability distribution, and then combines it with the inflation likelihood through Bayes’ rule to obtain its posterior distribution. In this work, we introduce a numerical implementation of this generic Bayesian approach that uses a particle filter (PF) to compute a Monte Carlo approximation of the inflation posterior distribution. To alleviate the sample attrition issue, the proposed PF employs an artificial dynamical model for the inflation factor based on the well-known smoothing-kernel West and Liu model. The positivity constraint on the inflation factor is further imposed through an inverse-Gamma transition density, whose parameters suggest analytical expressions. The resulting PF-EnKF scheme is straightforward to implement, and can use different number of particles in its EnKF and PF components. Numerical experiments are conducted with the Lorenz-96 model to demonstrate the effectiveness of the proposed method under various experimental scenarios.

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