Ab Initio and Monte Carlo Approaches For the Magnetocaloric Effect in Co- and In-Doped Ni-Mn-Ga Heusler Alloys

This special issue collects contributions from the participants of the “Information in Dynamical Systems and Complex Systems” workshop, which cover a wide range of important problems and new approaches that lie in the intersection of information theory and dynamical systems. The contributions include theoretical characterization and understanding of the different types of information flow and causality in general stochastic processes, inference and identification of coupling structure and parameters of system dynamics, rigorous coarse-grain modeling of network dynamical systems, and exact statistical testing of fundamental information-theoretic quantities such as the mutual information. The collective efforts reported here in reflect a modern perspective of the intimate connection between dynamical systems and information flow, leading to the promise of better understanding and modeling of natural complex systems and better/optimal design of engineering systems.

[1]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[2]  James P. Crutchfield,et al.  Information Anatomy of Stochastic Equilibria , 2014, Entropy.

[3]  Dane Taylor,et al.  Causal Network Inference by Optimal Causation Entropy , 2014, SIAM J. Appl. Dyn. Syst..

[4]  Erik M. Bollt,et al.  Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings , 2014, 1504.03769.

[5]  Milan Palus,et al.  Cross-Scale Interactions and Information Transfer , 2014, Entropy.

[6]  W. Parry Intrinsic Markov chains , 1964 .

[7]  James P. Crutchfield,et al.  Infinite Excess Entropy Processes with Countable-State Generators , 2011, Entropy.

[8]  S. Yoshizawa,et al.  An Active Pulse Transmission Line Simulating Nerve Axon , 1962, Proceedings of the IRE.

[9]  M. Gameiro,et al.  Combinatorial-topological framework for the analysis of global dynamics. , 2012, Chaos.

[10]  K. Hlavácková-Schindler,et al.  Causality detection based on information-theoretic approaches in time series analysis , 2007 .

[11]  Konstantin Mischaikow,et al.  A Database Schema for the Analysis of Global Dynamics of Multiparameter Systems , 2009, SIAM J. Appl. Dyn. Syst..

[12]  A. Kolmogorov Three approaches to the quantitative definition of information , 1968 .

[13]  Dimitris Kugiumtzis,et al.  Non-uniform state space reconstruction and coupling detection , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  M. Paluš,et al.  Inferring the directionality of coupling with conditional mutual information. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  Adom Giffin,et al.  Simultaneous State and Parameter Estimation Using Maximum Relative Entropy with Nonhomogenous Differential Equation Constraints , 2014, Entropy.

[16]  Erik M. Bollt,et al.  Applied and Computational Measurable Dynamics , 2013, Mathematical modeling and computation.

[17]  James P. Crutchfield,et al.  Anatomy of a Bit: Information in a Time Series Observation , 2011, Chaos.

[18]  Shawn D. Pethel,et al.  Exact Test of Independence Using Mutual Information , 2014, Entropy.

[19]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.

[20]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[21]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[22]  Emmanuel J. Candès,et al.  Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies? , 2004, IEEE Transactions on Information Theory.

[23]  Gregory J. Chaitin,et al.  On the Length of Programs for Computing Finite Binary Sequences , 1966, JACM.

[24]  Sachit Butail,et al.  Information Flow in Animal-Robot Interactions , 2014, Entropy.

[25]  M. Elowitz,et al.  A synthetic oscillatory network of transcriptional regulators , 2000, Nature.

[26]  Y. Lai,et al.  What symbolic dynamics do we get with a misplaced partition? On the validity of threshold crossings analysis of chaotic time-series , 2001 .

[27]  S. D. Pethel,et al.  Exact significance test for Markov order , 2014 .

[28]  M Palus,et al.  Synchronization as adjustment of information rates: detection from bivariate time series. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  Xiao Wang,et al.  Identifying Chaotic FitzHugh-Nagumo Neurons Using Compressive Sensing , 2014, Entropy.

[30]  Dimitris Kugiumtzis,et al.  Nearest neighbor estimate of conditional mutual information in feature selection , 2012, Expert Syst. Appl..

[31]  Jie Sun,et al.  Identifying the Coupling Structure in Complex Systems through the Optimal Causation Entropy Principle , 2014, Entropy.

[32]  Konstantin Mischaikow,et al.  Coarse Dynamics for Coarse Modeling: An Example From Population Biology , 2014, Entropy.

[33]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.