1,720,966 research outputs found
Bayesian inversion of coupled radiative and heat transfer models for asteroid regoliths and lakes
No full textestimation, the quantities we seek to retrieve are considered as random variables. The randomness includes the uncertainty regarding
their true values.Weintend to use this approach to perform inversion of coupled radiative and heat transfer models for asteroid regoliths and lakes. The Bayesian inversion of this kind of models allows estimating optical and thermodynamic properties of the systems considered, and also allows finding any correlation among these properties; that would be quite difficult to find with the classical approaches
Pontryagin Neural Networks for the Class of Optimal Control Problems With Integral Quadratic Cost
Full text linkThis work introduces Pontryagin Neural Networks (PoNNs), a specialised subset of Physics-Informed Neural Networks (PINNs) that aim to learn optimal control actions for optimal control problems (OCPs) characterised by integral quadratic cost functions. PoNNs employ the Pontryagin Minimum Principle (PMP) to establish necessary conditions for optimality, resulting in a two-point boundary value problem (TPBVP) that involves both state and costate variables within a system of ordinary differential equations (ODEs). By modelling the unknown solutions of the TPBVP with neural networks, PoNNs effectively learn the optimal control strategies. We also derive upper bounds on the generalisation error of PoNNs in solving these OCPs, taking into account the selection and quantity of training points along with the training error. To validate our theoretical analysis, we perform numerical experiments on benchmark linear and nonlinear OCPs. The results indicate that PoNNs can successfully learn open-loop control actions for the considered class of OCPs, outperforming the commercial software GPOPS-II in terms of both accuracy and computational efficiency. The reduced computational time suggests that PoNNs hold promise for real-time applications
Physics-informed Neural Networks for Optimal Intercept Problem,
No full textThe novel Extreme Theory of Functional Connections (X-TFC) method is employed to solve the optimal intercept
problem. With X-TFC, for the first time, Theory of Functional Connections (TFC) and shallow Neural Networks (NNs)
trained via the Extreme Learning Machine (ELM) algorithm are brought together as a class of PINN methods and applied to solving a broad class of ODEs and PDEs. In particular, the unknown solutions (in strong sense) of the ODEs and PDEs are approximated via particular expressions, called constrained expression (CEs), defined within TFC. A CE is a functional that always analytically satisfies the specified constraints and has a free-function that does not affect the specified constraints. In the X-TFC method, the free-function is a single-layer NN, trained via ELM algorithm. According to the ELM algorithm, the unknown constant coefficients appear linearly and thus, a least-squares method (for linear problems) or an iterative least-square method (for nonlinear problems) is used to compute the unknowns by minimizing the residual of the differential equations. In this work, the differential equations are represented by the system arising from the indirect method formulation of optimal control problems, which exploits the Hamiltonian function and the Pontryagin Maximum/Minimum Principle (PMP) to obtain a Two-Point Boundary Value Problem. The proposed method is tested by solving the Feldbaum problem and the minimum time-energy optimal intercept problem. It is shown that the major advantage of this method is the comparable accuracy with respect to the state of the art methods for the solution of optimal control problems along with an extremely fast computational time. In particular, the low computational time makes the proposed method suitable for real-time applications
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
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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Extreme Theory of Functional Connections for Problems Involving Differential Equations with Applications to Optimal Control Problems
No full textIn recent years, Neural Networks (NNs) have become widespread across many scientific fields. This requires designing them to satisfy application-specific constraints, such as conservation laws, symmetries, or other domain-specific knowledge. These constraints are usually imposed as soft penalties during network training and act as regularizers within the loss function. Physics-Informed Neural Networks (PINNs) are examples of this philosophy, where the outputs of the network are constrained to approximately satisfy specific physics laws, modeled as a set of Differential Equations (DEs).This dissertation presents a novel, accurate, fast, flexible, reliable, and robust PINN framework for forward and inverse problems governed by DEs. This framework is called Extreme Theory of Functional Connections (X-TFC). The proposed method, for the first time, merges the Theory of Functional Connections (TFC) and the Extreme Learning Machines (ELM). TFC enables functional interpolation for a large class of mathematical objects. According to TFC, any mathematical problem solution can be represented via the Constrained Expressions (CEs). The CEs are functional. These functionals are the sum of a free function and a functional that analytically satisfies the problem constraints. When applied to problems involving DEs, the mathematical problem is represented by the DE itself, where the constraints are the DE initial and/or boundary conditions. The DE solution is approximated with the CE, where the free function, according to X-TFC, is chosen to be a shallow NN trained via the Extreme Learning Machine (ELM) algorithm. ELM algorithm randomly selects input weights and biases, which are not tuned during the training, leaving the output weights as the only trainable parameters. Thus, the training is performed with a fast and robust least-squares.
This work shows the X-TFC advantage over the classic PINN framework and some of its variants. In particular, it will show many X-TFC applications in solving forward and inverse problems involving DEs. X-TFC has been applied to physics-driven solutions of nuclear reactor dynamics (e.g., point kinetic equations with temperature feedback). Another application is for physics-driven solutions of Optimal Control Problems (OCPs), both via the application of the indirect method (e.g., Pontryagin Minimum Principle) and the Bellman Principle of Optimality (BPO), both for general and aerospace OCPs. X-TFC has also been applied for data-physics-driven parameter discovery of epidemiological models that regulate virus spread.Dissertation not available (per author's request
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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