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1579 Publications visible to you, out of a total of 1579

Abstract

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Author: Ainara Claveras Cabezudo

Date Published: 2022

Publication Type: Master's Thesis

Abstract (Expand)

Branched Optimal Transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in transport efficiency when shipping mass along the same route, favoring branched transportation networks. We here study the NP-hard optimization of BOT networks connecting a finite number of sources and sinks in ℝ2. First, we show how to efficiently find the best geometry of a BOT network for many sources and sinks, given a topology. Second, we argue that a topology with more than three edges meeting at a branching point is never optimal. Third, we show that the results obtained for the Euclidean plane generalize directly to optimal transportation networks on two-dimensional Riemannian manifolds. Finally, we present a simple but effective approximate BOT solver combining geometric optimization with a combinatorial optimization of the network topology.

Authors: Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht

Date Published: 2022

Publication Type: Journal

Abstract

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Authors: Regine Nessel, Thorsten Löffler, Johannes Rinn, Philipp Lösel, Samuel Voss, Vincent Heuveline, Matthias Vollmer, Johannes Görich, Yannique-Maximilian Ludwig, Luai Al-Hileh, Friedrich Kallinowski

Date Published: 15th Dec 2021

Publication Type: Journal

Abstract

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Authors: S. Kashif Sadiq, Abraham Muñiz Chicharro, Patrick Friedrich, Rebecca C. Wade

Date Published: 14th Dec 2021

Publication Type: Journal

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EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.

Authors: J. Range, C. Halupczok, J. Lohmann, N. Swainston, C. Kettner, F. T. Bergmann, A. Weidemann, U. Wittig, S. Schnell, J. Pleiss

Date Published: 11th Dec 2021

Publication Type: Journal

Abstract (Expand)

This thesis presents multidimensional hydrodynamic simulations of stellar interiors with a focus on dynamical phenomena at low Mach numbers using the Seven-League Hydro (SLH) code. A better understanding of these phenomena is crucial to improve the modeling of stellar evolution. It is demonstrated that suitable numerical methods are required to avoid that numerical artifacts and spurious dissipation dominate the actual physical flow. Three-dimensional simulations of convective helium shell burning are used to measure the entrainment of mass into the convective region. This aids the parametrization of entrainment in one-dimensional evolution calculations. Furthermore, the excitation of waves by core convection is simulated and the results are analyzed regarding their physical plausibility and agreement with observations. These simulations demonstrate that the current set of numerical methods used by the SLH code is promising regarding future, more realistic simulations of astrophysical flows.

Author: Leonhard Horst

Date Published: 10th Dec 2021

Publication Type: Doctoral Thesis

Abstract

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Authors: Federico López, Beatrice Pozzetti, Steve Trettel, Michael Strube, Anna Wienhard

Date Published: 6th Dec 2021

Publication Type: InProceedings

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