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A FullyAccelerated Surface Integral Equation Method for Modeling Electromagnetic Scattering from Arbitrary Objects arXiv.cs.CE Pub Date : 20200325
Shashwat Sharma; Piero TriverioSurface integral equation (SIE) methods are of great interest for the numerical solution of Maxwell's equations in the presence of homogeneous objects. However, existing SIE algorithms have limitations, either in terms of scalability, frequency range, or material properties. We present a scalable SIE algorithm based on the generalized impedance boundary condition which can efficiently handle, in a

Position based dynamic of a particle system: a configurable algorithm to describe complex behaviour of continuum material starting from swarm robotics arXiv.cs.CE Pub Date : 20200324
Ramiro dell'ErbaIn a previous work we considered a twodimensional lattice of particles and calculated its time evolution by using an interaction law based on the spatial position of the particles themselves. The model reproduced the behaviour of deformable bodies both according to the standard Cauchy model and second gradient theory; this success led us to use this method in more complex cases. This work is intended

XBlockEOS: Extracting and Exploring Blockchain Data From EOSIO arXiv.cs.CE Pub Date : 20200326
Weilin Zheng; Zibin Zheng; HongNing Dai; Xu Chen; Peilin ZhengBlockchainbased cryptocurrencies and applications have flourished the blockchain research community. Massive data generated from diverse blockchain systems bring not only huge business values and but also technical challenges in data analytics of heterogeneous blockchain data. Different from Bitcoin and Ethereum, EOSIO has richer diversity and higher volume of blockchain data due to its unique architectural

Selective review of offline change point detection methods arXiv.cs.CE Pub Date : 20180102
Charles Truong; Laurent Oudre; Nicolas VayatisThis article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes

freud: A Software Suite for High Throughput Analysis of Particle Simulation Data arXiv.cs.CE Pub Date : 20190614
Vyas Ramasubramani; Bradley D. Dice; Eric S. Harper; Matthew P. Spellings; Joshua A. Anderson; Sharon C. GlotzerThe freud Python package is a powerful library for analyzing simulation data. Written with modern simulation and data analysis workflows in mind, freud provides a Python interface to fast, parallelized C++ routines that run efficiently on laptops, workstations, and supercomputing clusters. The package provides the core tools for finding particle neighbors in periodic systems, and offers a uniform API

Automatic Modelling of Human Musculoskeletal Ligaments  Framework Overview and Model Quality Evaluation arXiv.cs.CE Pub Date : 20200324
Noura Hamze; Lukas Nocker; Nikolaus Rauch; Markus Walzthöni; Fabio Carrillo; Philipp Fürnstahl; Matthias HardersAccurate segmentation of connective soft tissues is still a challenging task, which hinders the generation of corresponding geometric models for biomechanical computations. Alternatively, one could predict ligament insertion sites and then approximate the shapes, based on anatomical knowledge and morphological studies. Here, we describe a corresponding integrated framework for the automatic modelling

A machine learning accelerated FE$^2$ homogenization algorithm for elastic solids arXiv.cs.CE Pub Date : 20200321
Saumik Dana; Mary F WheelerThe FE$^2$ homogenization algorithm for multiscale modeling iterates between the macroscale and the microscale (represented by a representative volume element) till convergence is achieved at every increment of macroscale loading. The information exchange between the two scales occurs at the gauss points of the macroscale finite element discretization. The microscale problem is also solved using finite

Intelligent multiscale simulation based on processguided composite database arXiv.cs.CE Pub Date : 20200320
Zeliang Liu; Haoyan Wei; Tianyu Huang; C. T. WuIn the paper, we present an integrated datadriven modeling framework based on process modeling, material homogenization, mechanistic machine learning, and concurrent multiscale simulation. We are interested in the injectionmolded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries. The molding process induces spatially

On the scalability of CFD tool for supersonic jet flow configurations arXiv.cs.CE Pub Date : 20200318
Carlos JunqueiraJunior; João Luiz F. Azevedo; Jairo Panetta; William R. Wolf; Sami YamouniNew regulations are imposing noise emissions limitations for the aviation industry which are pushing researchers and engineers to invest efforts in studying the aeroacoustics phenomena. Following this trend, an inhouse computational fluid dynamics tool is build to reproduce high fidelity results of supersonic jet flows for aeroacoustic analogy applications. The solver is written using the large eddy

A multilevel Monte Carlo method for highdimensional uncertainty quantification of lowfrequency electromagnetic devices arXiv.cs.CE Pub Date : 20180324
Armin Galetzka; Zeger Bontinck; Ulrich Römer; Sebastian SchöpsThis work addresses uncertainty quantification of electromagnetic devices determined by the eddy current problem. The multilevel Monte Carlo (MLMC) method is used for the treatment of uncertain parameters while the devices are discretized in space by the finite element method. Both methods yield numerical approximations such that the total errors is split into stochastic and spatial contributions.

Multiphysics Simulation of Plasmonic Photoconductive Antennas using Discontinuous Galerkin Methods arXiv.cs.CE Pub Date : 20191208
Liang Chen; Hakan BagciPlasmonic nanostructures significantly improve the performance of photoconductive antennas (PCAs) in generating terahertz radiation. However, they are geometrically intricate and result in complicated electromagnetic (EM) field and carrier interactions under a bias voltage and upon excitation by an optical EM wave. These lead to new challenges in simulations of plasmonic PCAs, which cannot be addressed

Strong Scaling of Numerical Solver for Supersonic Jet Flow Configuration arXiv.cs.CE Pub Date : 20200319
Carlos JunqueiraJunior; João Luiz F. Azevedo; Jairo Panetta; William R. Wolf; Sami YamouniAcoustics loads are rocket design constraints which push researches and engineers to invest efforts in the aeroacoustics phenomena which is present on launch vehicles. Therefore, an inhouse computational fluid dynamics tool is developed in order to reproduce highfidelity results of supersonic jet flows for aeroacoustic analogy applications. The solver is written using the large eddy simulation formulation

A Hybrid Phase Field Model for Fracture Induced by Lithium Diffusion in Electrode Particles of Liion Batteries arXiv.cs.CE Pub Date : 20200317
Masoud AhmadiLithiumion batteries (LIBs) of high energy density and lightweight design, have found wide applications in electronic devices and systems. Degradation mechanisms that caused by lithiation is a main challenging problem for LIBs with high capacity electrodes like silicon (Si), which eventually can reduce the lifetime of batteries. In this paper, a hybrid phase field model (PFM) is proposed to study

Inference of Gasliquid Flowrate using Neural Networks arXiv.cs.CE Pub Date : 20200315
Akshay J. DaveMassachusetts Institute of Technology; Annalisa ManeraUniversity of Michigan AnnArborThe metering of gasliquid flows is difficult due to the nonlinear relationship between flow regimes and fluid properties, flow orientation, channel geometry, etc. In fact, a majority of commercial multiphase flow meters have a low accuracy, limited range of operation or require a physical separation of the phases. We introduce the inference of gasliquid flowrates using a neural network model that

Shape Optimization of Rotating Electric Machines using Isogeometric Analysis and Harmonic StatorRotor Coupling arXiv.cs.CE Pub Date : 20190815
Melina Merkel; Peter Gangl; Sebastian SchöpsThis work deals with shape optimization of electric machines using isogeometric analysis. Isogeometric analysis is particularly well suited for shape optimization as it allows to easily modify the geometry without remeshing the domain. A 6pole permanent magnet synchronous machine is modeled using a multipatch isogeometric approach and rotation of the machine is realized by modeling the stator and

Stencil scaling for vectorvalued PDEs on hybrid grids with applications to generalized Newtonian fluids arXiv.cs.CE Pub Date : 20190823
Daniel Drzisga; Ulrich Rüde; Barbara WohlmuthMatrixfree finite element implementations for large applications provide an attractive alternative to standard sparse matrix data formats due to the significantly reduced memory consumption. Here, we show that they are also competitive with respect to the run time in the low order case if combined with suitable stencil scaling techniques. We focus on variable coefficient vectorvalued partial differential

Datadriven surrogate modelling and benchmarking for process equipment arXiv.cs.CE Pub Date : 20200313
Gabriel F. N. Gonçalves; Assen Batchvarov; Yuyi Liu; Yuxin Liu; Lachlan Mason; Indranil Pan; Omar K. MatarA suite of computational fluid dynamics (CFD) simulations geared towards chemical process equipment modelling has been developed and validated with experimental results from the literature. Various regression based active learning strategies are explored with these CFD simulators intheloop under the constraints of a limited function evaluation budget. Specifically, five different sampling strategies

A local basis approximation approach for nonlinear parametric model order reduction arXiv.cs.CE Pub Date : 20200316
Konstantinos Vlachas; Konstantinos Tatsis; Konstantinos Agathos; Adam R. Brink; Eleni ChatziThe efficient condition assessment of engineered systems requires the coupling of high fidelity models with data extracted from the state of the system `asis'. In enabling this task, this paper implements a parametric Model Order Reduction (pMOR) scheme for nonlinear structural dynamics, and the particular case of material nonlinearity. A physicsbased parametric representation is developed, incorporating

Pressio: Enabling projectionbased model reduction for largescale nonlinear dynamical systems arXiv.cs.CE Pub Date : 20200317
Francesco Rizzi; Patrick J. Blonigan; Kevin T. CarlbergThis work introduces Pressio, an opensource project aimed at enabling leadingedge projectionbased reduced order models (ROMs) for largescale nonlinear dynamical systems in science and engineering. Pressio provides modelreduction methods that can reduce both the number of spatial and temporal degrees of freedom for any dynamical system expressible as a system of parameterized ordinary differential

Effective Response of Heterogeneous Materials using the Recursive Projection Method arXiv.cs.CE Pub Date : 20200306
Xiaoyao Peng; Dhriti Nepal; Kaushik DayalThis paper applies the Recursive Projection Method (RPM) to the problem of finding the effective mechanical response of a periodic heterogeneous solid. Previous works apply the Fast Fourier Transform (FFT) in combination with various fixedpoint methods to solve the problem on the periodic unit cell. These have proven extremely powerful in a range of problems ranging from imagebased modeling to dislocation

Efficient Parallel Simulation of Blood Flows in Abdominal Aorta arXiv.cs.CE Pub Date : 20190610
Shanlin Qin; Rongliang Chen; Bokai Wu; Jia Liu; WenShin Shiu; Zhengzheng Yan; XiaoChuan CaiIt is known that the maximum diameter for the rupturerisk assessment of the abdominal aortic aneurysm is a generally good method, but not sufficient. Alternative features obtained with computational modeling may provide additional useful criteria. Though computational approaches are noninvasive, they are often timeconsuming because of the high computational complexity. In this paper, we present a

Similarities and Evolutionary Relationships of COVID19 and Related Viruses arXiv.cs.CE Pub Date : 20200312
Yanni Li; Bing Liu; Jiangtao Cui; Zhi Wang; Yulong Shen; Yueshen Xu; Kaicheng YaoWe have collected a large set of 377 publicly available complete genome sequences of the COVID19 virus, the previously known flucausing coronaviruses, HCov229E, HCovOC43, HCovNL63 and HCovHKU1, and the deadly pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue. This article reports a computational study of the similarities and the evolutionary relationships of COVID19

Estimation of lateral track irregularity through Kalman filtering techniques arXiv.cs.CE Pub Date : 20200311
S. Munoz; J. Ros; J. L. EscalonaThe aim of this work is to develop a modelbased methodology for monitoring lateral track irregularities based on the use of inertial sensors mounted on an inservice train. To this end, a gyroscope is used to measure the wheelset yaw angular velocity and two accelerometers are used to measure lateral acceleration of the wheelset and the bogie frame. Using a highly simplified linear bogie model that

Benchmark Dataset for MidPrice Forecasting of Limit Order Book Data with Machine Learning Methods arXiv.cs.CE Pub Date : 20170509
Adamantios Ntakaris; Martin Magris; Juho Kanniainen; Moncef Gabbouj; Alexandros IosifidisManaging the prediction of metrics in highfrequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of highfrequency limit order markets for midprice prediction. We extracted normalized data representations of time series data for five

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain arXiv.cs.CE Pub Date : 20200305
Zhenguo Nie; Tong Lin; Haoliang Jiang; Levent Burak KaraIn topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results. We propose a new datadriven topology optimization model called TopologyGAN that takes advantage of various physical fields computed on the original, unoptimized

ASAPSML: An Antibody Sequence Analysis Pipeline Using Statistical Testing and Machine Learning arXiv.cs.CE Pub Date : 20200308
Xinmeng Li; James A. Van Deventer; Soha HassounAntibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibodybased inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning

Enhancing Industrial Xray Tomography by DataCentric Statistical Methods arXiv.cs.CE Pub Date : 20200308
Jarkko Suuronen; Muhammad Emzir; Sari Lasanen; Simo Särkkä; Lassi RoininenXray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, chemical engineering, and geotechnical engineering. In this article, we study Bayesian methods for the Xray tomography reconstruction. In Bayesian methods, the inverse problem of tomographic reconstruction is solved with help of a statistical prior distribution which encodes the possible

An energy stable onefield monolithic arbitrary LagrangianEulerian formulation for fluidstructure interaction arXiv.cs.CE Pub Date : 20200308
Yongxing Wang; Peter K. Jimack; Mark A. Walkley; Olivier PironneauIn this article we present a onefield monolithic finite element method in the Arbitrary LagrangianEulerian (ALE) formulation for FluidStructure Interaction (FSI) problems. The method only solves for one velocity field in the whole FSI domain, and it solves in a monolithic manner so that the fluid solid interface conditions are satisfied automatically. We prove that the proposed scheme is unconditionally

Apollo: A SequencingTechnologyIndependent, Scalable, and Accurate Assembly Polishing Algorithm arXiv.cs.CE Pub Date : 20190212
Can Firtina; Jeremie S. Kim; Mohammed Alser; Damla Senol Cali; A. Ercument Cicek; Can Alkan; Onur MutluLong reads produced by thirdgeneration sequencing technologies are used to construct an assembly (i.e., the subject's genome), which is further used in downstream genome analysis. Unfortunately, long reads have high sequencing error rates and a large proportion of bps in these long reads are incorrectly identified. These errors propagate to the assembly and affect the accuracy of genome analysis.

Bayesian optimization of variablesize design space problems arXiv.cs.CE Pub Date : 20200306
Julien Pelamatti; Loic Brevault; Mathieu Balesdent; ElGhazali Talbi; Yannick GuerinWithin the framework of complex system design, it is often necessary to solve mixed variable optimization problems, in which the objective and constraint functions can depend simultaneously on continuous and discrete variables. Additionally, complex system design problems occasionally present a variablesize design space. This results in an optimization problem for which the search space varies dynamically

Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning arXiv.cs.CE Pub Date : 20200306
Rui Zhou; Shiji Song; Anke Xue; Keyou You; Hu WuDuring recent decades, the automatic train operation (ATO) system has been gradually adopted in many subway systems. On the one hand, it is more intelligent than traditional manual driving; on the other hand, it increases the energy consumption and decreases the riding comfort of the subway system. This paper proposes two smart train operation algorithms based on the combination of expert knowledge

Bayesreef: A Bayesian inference framework for modelling reef growth in response to environmental change and biological dynamics arXiv.cs.CE Pub Date : 20180806
Jodie Pall; Rohitash Chandra; Danial Azam; Tristan Salles; Jody M. Webster; Richard Scalzo; Sally CrippsEstimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReefCore is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef

Fast uncertainty quantification of tracer distribution in the brain interstitial fluid with multilevel and quasi Monte Carlo arXiv.cs.CE Pub Date : 20200304
Matteo Croci; Vegard Vinje; Marie E. RognesMathematical models in biology involve many parameters that are uncertain or in some cases unknown. Over the last years, increased computing power has expanded the complexity and increased the number of degrees of freedom of many such models. For this reason, efficient uncertainty quantification algorithms are now needed to explore the often large parameter space of a given model. Advanced Monte Carlo

Markov Chain Monte Carlo with Neural Network Surrogates: Application to Contaminant Source Identification arXiv.cs.CE Pub Date : 20200301
Zitong Zhou; Daniel M. TartakovskySubsurface remediation often involves reconstruction of contaminant release history from sparse observations of solute concentration. Markov Chain Monte Carlo (MCMC), the most accurate and general method for this task, is rarely used in practice because of its high computational cost associated with multiple solves of contaminant transport equations. We propose an adaptive MCMC method, in which a transport

Inline Vector Compression for Computational Physics arXiv.cs.CE Pub Date : 20200227
Will Trojak; Freddie WitherdenA novel inline data compression method is presented for singleprecision vectors in three dimensions. The primary application of the method is for accelerating computational physics calculations where the throughput is bound by memory bandwidth. The scheme employs spherical polar coordinates, angle quantisation, and a bespoke floatingpoint representation of the magnitude to achieve a fixed compression

Modified Bee Colony optimization algorithm for computational parameter identification for pore scale transport in periodic porous media arXiv.cs.CE Pub Date : 20200302
Vasiliy V. Grigoriev; Oleg Iliev; Petr N. VabishchevichThis paper discusses an optimization method called Modified Bee Colony algorithm (MBC) based on a particular intelligent behavior of honeybee swarms. The algorithm was checked in a few benchmarks like Shekel, Rozenbroke, Himmelblau and Rastrigin functions, then was applied to parameter identification for reactive flow problems in periodic porous media. The simulation results show that the performance

A deep learning framework for solution and discovery in solid mechanics: linear elasticity arXiv.cs.CE Pub Date : 20200214
Ehsan Haghighat; Maziar Raissi; Adrian Moure; Hector Gomez; Ruben JuanesWe present the application of a class of deep learning, known as Physics Informed Neural Networks (PINN), to learning and discovery in solid mechanics. We explain how to incorporate the momentum balance and constitutive relations into PINN, and explore in detail the application to linear elasticity, although the framework is rather general and can be extended to other solidmechanics problems. While

Multichannel Analysis of Surface Waves Accelerated (MASWAccelerated): Software for Efficient Surface Wave Inversion Using MPI and GPUs arXiv.cs.CE Pub Date : 20200304
Joseph Kump; Eileen R. MartinMultichannel Analysis of Surface Waves (MASW) is a technique frequently used in geotechnical engineering and engineering geophysics to infer layered models of seismic shear wave velocities in the top tens to hundreds of meters of the subsurface. We aim to accelerate MASW calculations by capitalizing on modern computer hardware available in the workstations of most engineers: multiple cores and graphics

Benchmark for numerical solutions of flow in heterogeneous groundwater formations arXiv.cs.CE Pub Date : 20191125
Cristian D. Alecsa; Imre Boros; Florian Frank; Peter Knabner; Mihai Nechita; Alexander Prechtel; Andreas Rupp; Nicolae SuciuThis article presents numerical investigations on accuracy and convergence properties of several numerical approaches for simulating steady state flows in heterogeneous aquifers. Finite difference, finite element, discontinuous Galerkin, spectral, and random walk methods are tested on one and twodimensional benchmark flow problems. Realizations of lognormal hydraulic conductivity fields are generated

VulnDS: Topk Vulnerable SME Detection System in NetworkedLoans arXiv.cs.CE Pub Date : 20191228
Dawei Cheng; Xiaoyang Wang; Ying Zhang; Shunzhang WangGroups of small and medium enterprises (SMEs) can back each other to obtain loans from banks and thus form guarantee networks. If the loan repayment of a small company in the network defaults, its backers are required to repay the loan. Therefore, risk over networked enterprises may cause significant contagious damage. In realworld applications, it is critical to detect top vulnerable nodes in such

Variational inference formulation for a modelfree simulation of a dynamical system with unknown parameters by a recurrent neural network arXiv.cs.CE Pub Date : 20200302
Kyongmin Yeo; Dylan E. C. Grullon; FanKeng Sun; Duane S. Boning; Jayant R. KalagnanamWe propose a recurrent neural network for a "modelfree" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly learn the nonlinear time marching operator and the effects of the unknown parameters from a time series dataset. We assume that the time series data set consists of an ensemble of trajectories for a range of the parameters

A Hybrid LagrangianEulerian Method for Topology Optimization arXiv.cs.CE Pub Date : 20200302
Yue Li; Xuan Li; Minchen Li; Yixin Zhu; Bo Zhu; Chenfanfu JiangWe propose LETO, a new hybrid LagrangianEulerian method for topology optimization. At the heart of LETO lies in a hybrid particlegrid Material Point Method (MPM) to solve for elastic force equilibrium. LETO transfers density information from freely movable Lagrangian carrier particles to a fixed set of Eulerian quadrature points. The quadrature points act as MPM particles embedded in a lowerresolution

Framework of Fracture Network Modeling using Conditioned Data with Sequential Gaussian Simulation arXiv.cs.CE Pub Date : 20200303
Yerlan Amanbek; Timur Merembayev; Sanjay SrinivasanThe fracture characterization using a geostatistical tool with conditioning data is a computationally efficient tool for subsurface flow and transport applications. The main objective of the paper is to propose a framework of geostatistical method to model the fracture network. In the method, we have chosen neighborhood area to apply the Gaussian Sequential Simulation in order to generate the fracture

Imposing minimum and maximum member size, minimum cavity size, and minimum separation distance between solid members in topology optimization arXiv.cs.CE Pub Date : 20200229
Eduardo Fernández; Kaike Yang; Stijn Koppen; Pablo Alarcón; Simon Bauduin; Pierre DuysinxThis paper focuses on densitybased topology optimization and proposes a combined method to simultaneously impose Minimum length scale in the Solid phase (MinSolid), Minimum length scale in the Void phase (MinVoid) and Maximum length scale in the Solid phase (MaxSolid). MinSolid and MinVoid mean that the size of solid parts and cavities must be greater than the size of a prescribed circle or sphere

Inverse design of photonic crystals through automatic differentiation arXiv.cs.CE Pub Date : 20200301
Momchil Minkov; Ian A. D. Williamson; Lucio C. Andreani; Dario Gerace; Beicheng Lou; Alex Y. Song; Tyler W. Hughes; Shanhui FanGradientbased inverse design in photonics has already achieved remarkable results in designing smallfootprint, highperformance optical devices. The adjoint variable method, which allows for the efficient computation of gradients, has played a major role in this success. However, gradientbased optimization has not yet been applied to the modeexpansion methods that are the most common approach to

Data Normalization for Bilinear Structures in HighFrequency Financial Timeseries arXiv.cs.CE Pub Date : 20200301
Dat Thanh Tran; Juho Kanniainen; Moncef Gabbouj; Alexandros IosifidisFinancial timeseries analysis and forecasting have been extensively studied over the past decades, yet still remain as a very challenging research topic. Since financial market is inherently noisy and stochastic, a majority of financial timeseries of interests are nonstationary, and often obtained from different modalities. This property presents great challenges and can significantly affect the

Identification of abrupt stiffness changes of structures with tuned mass dampers under sudden events arXiv.cs.CE Pub Date : 20191219
S. SchleiterRWTH Aachen University; O. AltayRWTH Aachen UniversityThis paper presents a recursive system identification method for multidegreeoffreedom (MDoF) structures with tuned mass dampers (TMDs) considering abrupt stiffness changes in case of sudden events, such as earthquakes. Due to supplementary nonclassical damping of the TMDs, the system identification of MDoF+TMD systems disposes a challenge, in particular, in case of sudden events. This identification

On the presence of a critical detachment angle in gecko spatula peeling  A numerical investigation using an adhesive friction model arXiv.cs.CE Pub Date : 20200227
Saipraneeth Gouravaraju; Roger A. Sauer; Sachin Singh GautamA continuumbased computational contact model is employed to study coupled adhesion and friction in gecko spatulae. Nonlinear finite element analysis is carried out to simulate spatula peeling from a rigid substrate. It is shown that the "frictional adhesion" behavior, until now only observed from seta to toe levels, is also present at the spatula level. It is shown that for sufficiently small spatula

Exact artificial boundary conditions of 1D semidiscretized peridynamics arXiv.cs.CE Pub Date : 20200225
Songsong Ji; Gang Pang; Jiwei Zhang; Yibo Yang; Paris PerdikarisThe peridynamic theory reformulates the equations of continuum mechanics in terms of integrodifferential equations instead of partial differential equations. It is not trivial to directly apply naive approach in artificial boundary conditions for continua to peridynamics modeling, because it usually involves semidiscretization scheme. In this paper, we present a new way to construct exact boundary

Energy Resolved Neutron Imaging for Strain Reconstruction using the Finite Element Method arXiv.cs.CE Pub Date : 20200221
Riya Aggarwal; Mike Meylan; Bishnu Lamichhane; Chris WensrichA pulsed neutron imaging technique is used to reconstruct the residual strain within a polycrystalline material from Bragg edge strain images. This technique offers the possibility of a nondestructive analysis of strain fields with a high spatial resolution. A finite element approach is used to reconstruct the strain using the least square method constrained by the conditions of equilibrium. The procedure

Solution of option pricing equations using orthogonal polynomial expansion arXiv.cs.CE Pub Date : 20191213
Falko Baustian; Kateřina Filipová; Jan PospíšilIn this paper we study both analytic and numerical solutions of option pricing equations using systems of orthogonal polynomials. Using a Galerkinbased method, we solve the parabolic partial diferential equation for the BlackScholes model using Hermite polynomials and for the Heston model using Hermite and Laguerre polynomials. We compare obtained solutions to existing semiclosed pricing formulas

Machine Learning based prediction of noncentrosymmetric crystal materials arXiv.cs.CE Pub Date : 20200226
Yuqi Song; Joseph Lindsay; Yong Zhao; Alireza Nasiri; StephYves Loius; Jie Ling; Ming Hu; Jianjun HuNoncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However, the experimental discovery of new noncentrosymmetric materials is extremely difficult. Here we present a machine learning model that could predict whether the composition of a potential crystalline structure would be centrosymmetric

Wave propagation modeling in periodic elastothermodiffusive materials via multifield asymptotic homogenization arXiv.cs.CE Pub Date : 20200211
Francesca Fantoni; Andrea BacigalupoA multifield asymptotic homogenization technique for periodic thermodiffusive elastic materials is provided in the present study. Field equations for the firstorder equivalent medium are derived and overall constitutive tensors are obtained in closed form. These lasts depend upon the micro constitutive properties of the different phases composing the composite material and upon periodic perturbation

Using Reinforcement Learning in the Algorithmic Trading Problem arXiv.cs.CE Pub Date : 20200226
Evgeny Ponomarev; Ivan Oseledets; Andrzej CichockiThe development of reinforced learning methods has extended application to many areas including algorithmic trading. In this paper trading on the stock exchange is interpreted into a game with a Markov property consisting of states, actions, and rewards. A system for trading the fixed volume of a financial instrument is proposed and experimentally tested; this is based on the asynchronous advantage

Cell cycle and protein complex dynamics in discovering signaling pathways arXiv.cs.CE Pub Date : 20200226
Daniel Inostroza; Cecilia Hernández; Diego Seco; Gonzalo Navarro; Alvaro OliveraNappaSignaling pathways are responsible for the regulation of cell processes, such as monitoring the external environment, transmitting information across membranes, and making cell fate decisions. Given the increasing amount of biological data available and the recent discoveries showing that many diseases are related to the disruption of cellular signal transduction cascades, in silico discovery of signaling

Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data. Part I: Maximum A Posteriori Estimate arXiv.cs.CE Pub Date : 20200225
Amal Alghamdi; Marc A. Hesse; Jingyi Chen; Omar GhattasCharacterizing the properties of groundwater aquifers is essential for predicting aquifer response and managing groundwater resources. In this work, we develop a highdimensional scalable Bayesian inversion framework governed by a threedimensional quasistatic linear poroelastic model to characterize lateral permeability variations in groundwater aquifers. We determine the maximum a posteriori (MAP)

Joint geophysical, petrophysical and geologic inversion using a dynamic Gaussian mixture model arXiv.cs.CE Pub Date : 20200221
Thibaut Astic; Lindsey J. Heagy; Douglas W. OldenburgWe present a framework for petrophysically and geologically guided inversion to perform multiphysics joint inversions. Petrophysical and geological information is included in a multidimensional Gaussian mixture model that regularizes the inverse problem. The inverse problem we construct consists of a suite of three cyclic optimizations over the geophysical, petrophysical and geological information

Linearfrictional contact model for 3D discrete element simulations of granular systems arXiv.cs.CE Pub Date : 20200109
Matthew R. Kuhn; Kiichi Suzuki; Ali DaouadjiThe linearfrictional contact model is the most commonly used contact mechanism for discrete element (DEM) simulations of granular materials. Linear springs with a frictional slider are used for modeling interactions in directions normal and tangential to the contact surface. Although the model is simple in two dimensions, its implementation in 3D faces certain subtle challenges, and the particle interactions

FractionalOrder Models for the Static and Dynamic Analysis of Nonlocal Plates arXiv.cs.CE Pub Date : 20200219
Sansit Patnaik; Sai Sidhardh; Fabio SemperlottiThis study presents the analytical formulation and the finite element solution of fractional order nonlocal plates under both Mindlin and Kirchoff formulations. By employing consistent definitions for fractionalorder kinematic relations, the governing equations and the associated boundary conditions are derived based on variational principles. Remarkably, the fractionalorder nonlocal model gives

A subtractive manufacturing constraint for level set topology optimization arXiv.cs.CE Pub Date : 20200219
Nigel Morris; Adrian Butscher; Francesco IorioWe present a method for enforcing manufacturability constraints in generated parts such that they will be automatically ready for fabrication using a subtractive approach. We primarily target multiaxis CNC milling approaches but the method should generalize to other subtractive methods as well. To this end, we take as user input: the radius of curvature of the tool bit, a coarse model of the tool