A novel parameter for nonequilibrium analysis in reconstructed state spacesKernel methods are widely used for probability estimation by measuring the distribution of low-passed vector distances in reconstructed state spaces. However, the information conveyed by the vector…Wenpo Yao, Wenli Yao, Jun Wang·Oct 26, 2021SaveLearn
Identification of the nature of dynamical systems with recurrence plots and convolution neural networks: A preliminary testIn this study, we present a method for classifying dynamical systems using a hybrid approach involving recurrence plots and a convolution neural network (CNN). This is performed by obtaining the…Daniel Han, Giuseppe Orlando, Sergei Fedotov·Oct 20, 2021SaveLearn
Nuclear data evaluation with Bayesian networksBayesian networks are graphical models to represent the probabilistic relationships between variables in the Bayesian framework. The knowledge of all variables can be updated using new information…Georg Schnabel, Roberto Capote, Arjan Koning et al.·Oct 20, 2021SaveLearn
Vibrational quality classification of metallic turbine blades under measurement uncertaintyNon-destructive testing on metallic turbine blades is a challenging task due to their complex geometry. Vibrational test-ing such as Process Compensated Resonance Testing (PCRT) has shown an…Liangliang Cheng, Vahid Yaghoubi, Wim V. Paepegem et al.·Oct 18, 2021SaveLearn
Corrupted bifractal features in finite uncorrelated power-law distributed dataMultifractal Detrended Fluctuation Analysis stands out as one of the most reliable methods for unveiling multifractal properties, specially when real-world time series are under analysis. However,…Felipe Olivares, Massimiliano Zanin·Oct 14, 2021SaveLearn
Fourier-domain transfer entropy spectrumWe propose the Fourier-domain transfer entropy spectrum, a novel generalization of transfer entropy, as a model-free metric of causality. For arbitrary systems, this approach systematically…Yang Tian, Yaoyuan Wang, Ziyang Zhang et al.·Oct 13, 2021SaveLearn
Wind Farm Icing Loss Forecast Pertinent to Winter ExtremesThe 2021 Texas power crisis has highlighted the vulnerability of the power system under wind extremes, particularly with the increasing penetration of energy resources that depend on weather…Linyue Gao, Teja Dasari, Jiarong Hong·Oct 12, 2021SaveLearn
Universal uncertainty estimation for nuclear detector signals with neural networks and ensemble learningCharacterizing uncertainty is a common issue in nuclear measurement and has important implications for reliable physical discovery. Traditional methods are either insufficient to cope with the…Pengcheng Ai, Zhi Deng, Yi Wang et al.·Oct 11, 2021SaveLearn
Stochastic approach for assessing the predictability of chaotic time series using reservoir computingThe applicability of machine learning for predicting chaotic dynamics relies heavily upon the data used in the training stage. Chaotic time series obtained by numerically solving ordinary…Igor A Khovanov·Oct 11, 2021SaveLearn
Dynamical effects of inflation in ensemble-based data assimilation under the presence of model errorThe role of multiplicative and additive covariance inflation on ensemble dynamics under the presence of model errors is examined. We show that multiplicative inflation significantly impacts the…Scheffler Guillermo, Carrassi Alberto, Ruiz Juan et al.·Oct 7, 2021SaveLearn
NoLiTiA: An Open-Source Toolbox for Nonlinear Time Series AnalysisIn many scientific fields like e.g. neuroscience, climatology or physics, complex relationships can be described most parsimoniously by nonlinear mechanics. Despite their relevance, many scientists…Immo Weber, Carina Renate Oehrn·Oct 5, 2021SaveLearn
Paradigm Shift Through the Integration of Physical Methodology and Data ScienceData science methodologies, which have undergone significant developments recently, provide flexible representational performance and fast computational means to address the challenges faced by…Takashi Miyamoto·Sep 30, 2021SaveLearn
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHCWe develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study…Breno Orzari, Thiago Tomei, Maurizio Pierini et al.·Sep 30, 2021SaveLearn
Grassmannian diffusion maps based surrogate modeling via geometric harmonicsIn this paper, a novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics is developed for predicting the response of engineering systems and complex…Ketson R. M. dos Santos, Dimitrios G. Giovanis, Katiana Kontolati et al.·Sep 28, 2021SaveLearn
Inferring the shape of data: A probabilistic framework for analyzing experiments in the natural sciencesA critical step in data analysis for many different types of experiments is the identification of features with theoretically defined shapes in N-dimensional datasets; examples of this process…Korak Kumar Ray, Anjali R. Verma, Ruben L. Gonzalez et al.·Sep 26, 2021SaveLearn
Extreme events in dynamical systems and random walkers: A reviewExtreme events gain the attention of researchers due to their utmost importance in various contexts ranging from finance to climatology. This brings such recurrent events to the limelight of…Sayantan Nag Chowdhury, Arnob Ray, Syamal K. Dana et al.·Sep 23, 2021SaveLearn
Generalized Poincaré Orthogonality: A New Approach to POLSAR Data AnalysisIn this paper we outline a new approach to the analysis of polarimetric synthetic aperture (POLSAR) data. Here we exploit target orthogonality as a multi-dimensional extension of wave orthogonality,…Shane R. Cloude, Ashlin Richardson·Sep 19, 2021SaveLearn
Ariadne: PyTorch Library for Particle Track Reconstruction Using Deep LearningParticle tracking is a fundamental part of the event analysis in high energy and nuclear physics. Events multiplicity increases each year along with the drastic growth of the experimental data which…Pavel Goncharov, Egor Schavelev, Anastasia Nikolskaya et al.·Sep 18, 2021SaveLearn
Solving an elastic inverse problem using Convolutional Neural NetworksWe explore the application of a Convolutional Neural Network (CNN) to image the shear modulus field of an almost incompressible, isotropic, linear elastic medium in plane strain using displacement or…Nachiket H. Gokhale·Sep 16, 2021SaveLearn
Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at CollidersDeep neural networks have rightfully won the place of one of the most accurate analysis tools in high energy physics. In this paper we will cover several methods of improving the performance of a…Lev Dudko, Petr Volkov, Georgii Vorotnikov et al.·Sep 14, 2021SaveLearn
Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipelineRecent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the…Ayana Ghosh, Maxim Ziatdinov, Ondrej Dyck et al.·Sep 9, 2021SaveLearn
A deep learned nanowire segmentation model using synthetic data augmentationAutomatized object identification and feature analysis of experimental image data are indispensable for data-driven material science; deep-learning-based segmentation algorithms have been shown to be…Binbin Lin, Nima Emami, David A Santos et al.·Sep 9, 2021SaveLearn
Unravelling the origins of anomalous diffusion: from molecules to migrating storksAnomalous diffusion or, more generally, anomalous transport, with nonlinear dependence of the mean-squared displacement on the measurement time, is ubiquitous in nature. It has been observed in…Ohad Vilk, Erez Aghion, Tal Avgar et al.·Sep 9, 2021SaveLearn
Artificial intelligence for online characterization of ultrashort X-ray free-electron laser pulsesX-ray free-electron lasers (XFELs) as the world's brightest light sources provide ultrashort X-ray pulses with a duration typically in the order of femtoseconds. Recently, they have approached…Kristina Dingel, Thorsten Otto, Lutz Marder et al.·Aug 31, 2021SaveLearn
Towards automated extraction and characterization of scaling regions in dynamical systemsScaling regions -- intervals on a graph where the dependent variable depends linearly on the independent variable -- abound in dynamical systems, notably in calculations of invariants like the…Varad Deshmukh, Elizabeth Bradley, Joshua Garland et al.·Aug 31, 2021SaveLearn