1,720,960 research outputs found
PhysNODE: Fusion of data and expert knowledge for modeling dynamical systems
No full textBuilding a representative model of a complex system remains a highly challenging problem. While by now there is basic understanding of most physical domains, model design is often hindered by lack of detail, for example concerning model dimensions or its relevant constraints. Here we present a novel model-building approach – physNODE – augmenting basic system descriptions, based on expert knowledge in the form of ordinary differential equations, with continuous adjoint sensitivity analysis related to artificial neural network principles, based on observable data. With this we have created a general tool, that can be applied to any physical system described by ordinary differential equations. PhysNODE allows validating or extending the initial description, for example with different variables and constraints. This way one arrives at a better-optimised, representative lowdimensional model, which can fit existing data and predict novel experimental outcomes
AdoptODE: Fusion of data and expert knowledge for modeling dynamical systems
No full textBuilding a representative model of a complex system remains a highly challenging problem. While by now there is basic understanding of most physical domains, model design is often hindered by lack of detail, for example
concerning model dimensions or its relevant constraints. Here we present a novel model-building approach -- adoptODE -- augmenting basic system descriptions, based on expert knowledge in the form of ordinary differential equations, with continuous adjoint sensitivity analysis related to artificial neural network principles, based on observable data. With this we have created a general tool, that can be applied to any physical system described by ordinary differential equations. AdoptODE allows validating or extending the initial description, for example with different variables and constraints. This way one arrives at a better-optimised, representative low-dimensional model, which can fit existing data and predict novel experimental outcomes
Tutorial: a beginner’s guide to building a representative model of dynamical systems using the adjoint method
No full textBuilding a representative model of a complex dynamical system from empirical evidence remains a highly challenging problem. Classically, these models are described by systems of differential equations that depend on parameters that need to be optimized by comparison with data. In this tutorial, we introduce the most common multi-parameter estimation techniques, highlighting their successes and limitations. We demonstrate how to use the adjoint method, which allows efficient handling of large systems with many unknown parameters, and present prototypical examples across several fields of physics. Our primary objective is to provide a practical introduction to adjoint optimization, catering for a broad audience of scientists and engineers.Deutsche Forschungsgemeinschaft 501100001659Open-Access-Publikationsfonds 202
AdoptODE: Fusion of data and expert knowledge for modeling dynamical systems
No full textBuilding a representative model of a complex system remains a highly challenging problem. While by now there is basic understanding of most physical domains, model design is often hindered by lack of detail, for example
concerning model dimensions or its relevant constraints. Here we present a novel model-building approach -- adoptODE -- augmenting basic system descriptions, based on expert knowledge in the form of ordinary differential equations, with continuous adjoint sensitivity analysis related to artificial neural network principles, based on observable data. With this we have created a general tool, that can be applied to any physical system described by ordinary differential equations. AdoptODE allows validating or extending the initial description, for example with different variables and constraints. This way one arrives at a better-optimised, representative low-dimensional model, which can fit existing data and predict novel experimental outcomes
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Modeling Malaria Parasite Motility: From Chiral Active Particles to Complex Shapes with Surface Flow
No full textActive matter taps local energy sources to generate forces and motion. A key example is the locomotion of microorganisms, which can be modeled as active Brownian particles. A particularly intriguing case involves chiral active particles that follow a preferred sense of rotation. Working across relevant scales, I show theoretically that malaria parasites, owing to their high speeds and curved shape, provide an excellent model system for this class.
First, I built an automated image-processing pipeline to analyze experimentally measured trajectories in a three-dimensional hydrogel.
This established proof of uniformly right-handed chirality, which also controls transitions between two- and three-dimensional environments.
I then formulated a stochastic theory for chiral active particles based on an Ornstein–Uhlenbeck process for rotational dynamics, demonstrating that helical motion is more robust to fluctuations and can, statistically, yield larger net displacements—so that a helix can be ``straighter than a straight line”.
Finally, I developed a theory for the self-organized surface flow of adhesins, driving the motion. This suggested that the parasites’ curved shape is an evolutionary adaptation to avoid on-the-spot rotations. An extension of the theory that incorporates mechanical deformations attributes the observed right-handedness to an asymmetric release of adhesion molecules; this prediction was corroborated experimentally
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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