An information-theoretic approach to study fluid–structure interactions

2018 ◽  
Vol 848 ◽  
pp. 968-986 ◽  
Author(s):  
Peng Zhang ◽  
Maxwell Rosen ◽  
Sean D. Peterson ◽  
Maurizio Porfiri
Keyword(s):  
Information Transfer ◽  
Transfer Entropy ◽  
The Other ◽  
Theoretic Approach ◽  
Fluid Structure Interactions ◽  
Fluid Medium ◽  
Fluid Structure ◽  
Information Theoretic ◽  
Model Free ◽  
Information Theoretic Approach

The question of causality is pervasive to fluid–structure interactions, where it finds its most alluring instance in the study of fish swimming in coordination. How and why fish align their bodies, synchronize their motion, and position in crystallized formations are yet to be fully understood. Here, we posit a model-free approach to infer causality in fluid–structure interactions through the information-theoretic notion of transfer entropy. Given two dynamical units, transfer entropy quantifies the reduction of uncertainty in predicting the future state of one of them due to additional knowledge about the past of the other. We demonstrate our approach on a system of two tandem airfoils in a uniform flow, where the pitch angle of one airfoil is actively controlled while the other is allowed to passively rotate. Through transfer entropy, we seek to unveil causal relationships between the airfoils from information transfer conducted by the fluid medium.

A Transfer Entropy Based Approach for Fault Isolation in Industrial Robots

2021 ◽  
pp. 1-23
Author(s):  
Sathish Vallachira ◽  
Mikael Norrlof ◽  
Michal Orkisz ◽  
Sachit Butail
Keyword(s):  
Friction Coefficient ◽  
Spot Welding ◽  
Fault Isolation ◽  
Industrial Robots ◽  
Transfer Entropy ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Robotic Simulation ◽  
Pick And Place ◽  
Information Theoretic Approach

Abstract In this paper, we cast the problem of fault isolation in industrial robots as that of causal analysis within coupled dynamical processes and evaluate the efficacy of the information theoretic approach of transfer entropy. To create a realistic and exhaustive dataset, we simulate wear induced failure by increasing friction coefficient on select axes within an in-house robotic simulation tool that incorporates an elastic gearbox model. The source axis of failure is identified as one which has the highest net transfer entropy across all pairs of axes. In an exhaustive simulation study, we vary the friction successively in each axis across three common industrial tasks: pick and place, spot welding, and arc welding. Our results show that transfer entropy based approach is able to detect the axis of failure more than 80 percent of the time when the friction coefficient is 5% above the nominal value and always when friction coefficient is 10% above the nominal value. The transfer entropy approach is more than twice as accurate as cross-correlation, a classical time-series analysis used to identify directional dependence among processes.

scholarly journals Information Flow between Bitcoin and Other Investment Assets

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1116 ◽  
Author(s):  
Jang ◽  
Yi ◽  
Kim ◽  
Ahn
Keyword(s):  
Granger Causality ◽  
Information Flow ◽  
Transfer Entropy ◽  
Asset Markets ◽  
Theoretic Approach ◽  
Causality Test ◽  
Information Theoretic ◽  
Symbolic Transfer Entropy ◽  
Information Theoretic Approach

This paper studies the causal relationship between Bitcoin and other investment assets. We first test Granger causality and then calculate transfer entropy as an information-theoretic approach. Unlike the Granger causality test, we discover that transfer entropy clearly identifies causal interdependency between Bitcoin and other assets, including gold, stocks, and the U.S. dollar. However, for symbolic transfer entropy, the dynamic rise–fall pattern in return series shows an asymmetric information flow from other assets to Bitcoin. Our results imply that the Bitcoin market actively interacts with major asset markets, and its long-term equilibrium, as a nascent market, gradually synchronizes with that of other investment assets.

scholarly journals Transfer entropy as a tool for inferring causality from observational studies in epidemiology

2017 ◽  
Author(s):  
N. Ahmad Aziz
Keyword(s):  
Information Flow ◽  
Observational Studies ◽  
Epidemiological Data ◽  
Entropy Method ◽  
Transfer Entropy ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Clear Cut ◽  
Model Free ◽  
Obesity And Diabetes

AbstractRecently Wiener’s causality theorem, which states that one variable could be regarded as the cause of another if the ability to predict the future of the second variable is enhanced by implementing information about the preceding values of the first variable, was linked to information theory through the development of a novel metric called ‘transfer entropy’. Intuitively, transfer entropy can be conceptualized as a model-free measure of directed information flow from one variable to another. In contrast, directionality of information flow is not reflected in traditional measures of association which are completely symmetric by design. Although information theoretic approaches have been applied before in epidemiology, their value for inferring causality from observational studies is still unknown. Therefore, in the present study we use a set of simulation experiments, reflecting the most classical and widely used epidemiological observational study design, to validate the application of transfer entropy in epidemiological research. Moreover, we illustrate the practical applicability of this information theoretic approach to ‘real-world’ epidemiological data by demonstrating that transfer entropy is able to extract the correct direction of information flow from longitudinal data concerning two well-known associations, i.e. that between smoking and lung cancer and that between obesity and diabetes risk. In conclusion, our results provide proof-of-concept that the recently developed transfer entropy method could be a welcome addition to the epidemiological armamentarium, especially to dissect those situations in which there is a well-described association between two variables but no clear-cut inclination as to the directionality of the association.

scholarly journals Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 390
Author(s):  
Pouya Manshour ◽  
Georgios Balasis ◽  
Giuseppe Consolini ◽  
Constantinos Papadimitriou ◽  
Milan Paluš
Keyword(s):  
Solar Wind ◽  
Information Transfer ◽  
Linear Time ◽  
Vertical Component ◽  
Causal Link ◽  
Theoretic Approach ◽  
Geomagnetic Disturbances ◽  
Magnetospheric Substorms ◽  
Solar Wind Parameters ◽  
Information Theoretic Approach

An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth’s magnetosphere–ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.

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