From Logistic Growth to Exponential Growth in a Population Dynamical ModelDynamics among central sources (hubs) providing a resource and large number of components enjoying and contributing to this resource describes many real life situations. Modeling, controlling, and…Inbar Seroussi, Nir Sochen·Jul 31, 2019SaveLearn
Burst-tree decomposition of time series reveals the structure of temporal correlationsComprehensive characterization of non-Poissonian, bursty temporal patterns observed in various natural and social processes is crucial to understand the underlying mechanisms behind such temporal…Hang-Hyun Jo, Takayuki Hiraoka, Mikko Kivelä·Jul 31, 2019SaveLearn
Universality of power-law exponents by means of maximum likelihood estimationPower-law type distributions are extensively found when studying the behaviour of many complex systems. However, due to limitations in data acquisition, empirical datasets often only cover a narrow…Víctor Navas-Portella, Álvaro González, Isabel Serra et al.·Jul 30, 2019SaveLearn
Reducing the dependence of the neural network function to systematic uncertainties in the input spaceApplications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence of the neural…Stefan Wunsch, Simon Jörger, Roger Wolf et al.·Jul 26, 2019SaveLearn
Tackling limited simulation and small signalsWe present a new, analytic, Poisson likelihood derived, technique to account for the statistical uncertainties inherent in simulation samples of limited size. This method has better coverage…Carlos A. Argüelles, Austin Schneider, Tianlu Yuan·Jul 24, 2019SaveLearn
Spectral data analysis methods for the two-dimensional imaging diagnosticsSome spectral data analysis methods that are useful for the two-dimensional imaging diagnostics data are introduced. It is shown that the frequency spectrum, the local dispersion relation, the flow…Minjun J. Choi·Jul 22, 2019SaveLearn
A geometric approach to the transport of discontinuous densitiesDifferent observations of a relation between inputs ("sources") and outputs ("targets") are often reported in terms of histograms (discretizations of the source and the target densities).…Caroline Moosmüller, Felix Dietrich, Ioannis G. Kevrekidis·Jul 18, 2019SaveLearn
The permutation entropy and its applications on fire tests dataBased on the data gained from a full-scale experiment, the order/disorder characteristics of the compartment fire temperatures are analyzed. Among the known permutation/encoding type entropies used…Flavia-Corina Mitroi-Symeonidis, Ion Anghel, Octavian Lalu et al.·Jul 18, 2019SaveLearn
A machine learning framework for computationally expensive transient modelsThe promise of machine learning has been explored in a variety of scientific disciplines in the last few years, however, its application on first-principles based computationally expensive tools is…Prashant Kumar, Kushal Sinha, Nandkishor Nere et al.·Jul 12, 2019SaveLearn
Good and bad predictions: Assessing and improving the replication of chaotic attractors by means of reservoir computingThe prediction of complex nonlinear dynamical systems with the help of machine learning techniques has become increasingly popular. In particular, reservoir computing turned out to be a very…Alexander Haluszczynski, Christoph Räth·Jul 12, 2019SaveLearn
Spot the Difference: Accuracy of Numerical Simulations via the Human Visual SystemComparative evaluation lies at the heart of science, and determining the accuracy of a computational method is crucial for evaluating its potential as well as for guiding future efforts. However,…Kiwon Um, Xiangyu Hu, Bing Wang et al.·Jul 8, 2019SaveLearn
Precision annealing Monte Carlo methods for statistical data assimilation and machine learningIn statistical data assimilation (SDA) and supervised machine learning (ML), we wish to transfer information from observations to a model of the processes underlying those observations. For SDA, the…Zheng Fang, Adrian S. Wong, Kangbo Hao et al.·Jul 6, 2019SaveLearn
Equivalent Circuit Model Recognition of Electrochemical Impedance Spectroscopy via Machine LearningElectrochemical impedance spectroscopy (EIS) is an effective method for studying the electrochemical systems. The interpretation of EIS is the biggest challenge in this technology, which requires…Shan Zhu, Xinyang Sun, Yuxuan Wang et al.·Jul 3, 2019SaveLearn
Representing Model Discrepancy in Bound-to-Bound Data CollaborationWe extended the existing methodology in Bound-to-Bound Data Collaboration (B2BDC), an optimization-based deterministic uncertainty quantification (UQ) framework, to explicitly take into account model…Wenyu Li, Arun Hegde, James Oreluk et al.·Jul 1, 2019SaveLearn
Chi-squared Test for Binned, Gaussian SamplesWe examine the 2 test for binned, Gaussian samples, including effects due to the fact that the experimentally available sample standard deviation and the unavailable true standard deviation…Nicholas R. Hutzler·Jun 27, 2019SaveLearn
On the prediction of critical heat flux using a physics-informed machine learning-aided frameworkThe critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the abundance of…Xingang Zhao, Koroush Shirvan, Robert K. Salko et al.·Jun 26, 2019SaveLearn
Precursors to Rare Events in Stochastic ResonanceIn stochastic resonance, a periodically forced Brownian particle in a double-well potential jumps between minima at rare increments, the prediction of which poses a major theoretical challenge. Here,…L. T. Giorgini, S. H. Lim, W. Moon et al.·Jun 25, 2019SaveLearn
Designing compact training sets for data-driven molecular property predictionIn this paper, we consider the problem of designing a training set using the most informative molecules from a specified library to build data-driven molecular property models. Specifically, we use…Bowen Li, Srinivas Rangarajan·Jun 25, 2019SaveLearn
Data-driven prediction of vortex-induced vibration response of marine risers subjected to three-dimensional currentSlender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage.…Signe Riemer-Sørensen, Jie Wu, Halvor Lie et al.·Jun 24, 2019SaveLearn
Second derivative analysis and alternative data filters for multi-dimensional spectroscopies: a Fourier-space perspectiveThe second derivative image (SDI) method is widely applied to sharpen dispersive data features in multi-dimensional spectroscopies such as angle resolved photoemission spectroscopy (ARPES). Here, the…Rongjie Li, Xiaoni Zhang, Lin Miao et al.·Jun 23, 2019SaveLearn
Super-resolution energy spectra from neutron direct-geometry spectrometersNeutron direct-geometry time-of-flight chopper spectroscopy is instrumental in studying fundamental excitations of vibrational and/or magnetic origin. We report here that techniques in…Fahima Islam, Jiao Y. Y. Lin, Richard Archibald et al.·Jun 22, 2019SaveLearn
Uncertainty in the Predictive Capability of Detectors that Process Waveforms from ExplosionsExplosions near ground generate multiple geophysical waveforms in the radiation-dominated range of their signature fields. Multi-phenomological explosion monitoring (MultiPEM) at these ranges…Joshua D Carmichael, Robert J Nemzek·Jun 21, 2019SaveLearn
Horizon Visibility Graphs and Time Series Merge Trees are DualIn this paper we introduce the horizon visibility graph, a simple extension to the popular horizontal visibility graph representation of a time series, and show that it possesses a rigorous…Colin Stephen·Jun 20, 2019SaveLearn
A universal rank-order transform to extract signals from noisy dataWe introduce an ordinate method for noisy data analysis, based solely on rank information and thus insensitive to outliers. The method is nonparametric, objective, and the required data processing is…Glenn Ierley, Alex Kostinski·Jun 20, 2019SaveLearn
The 8-parameter Fisher-Bingham distribution on the sphereThe Fisher-Bingham distribution (FB8) is an eight-parameter family of probability density functions (PDF) on S2 that, under certain conditions, reduce to spherical analogues of…Tianlu Yuan·Jun 19, 2019SaveLearn