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Simulation model of spacetime with the Minkowski metric arXiv.cs.CE Pub Date : 20200922
Vasyliy I. GurianovIn this paper, we propose a simulation model of spacetime as a discrete model of physical space. The model is based on the ideas of Stephen Wolfram and uses nonnumerical modelling. The simulation model is described as an ontology. We use objectoriented simulation (OOS), but the model is also suitable for agentbased simulation (ABS). We use UML2 SP (UML Scientific Profile), an objectoriented simulation

Towards realtime finitestrain anisotropic thermoviscoelastodynamic analysis of soft tissues for thermal ablative therapy arXiv.cs.CE Pub Date : 20200922
Jinao Zhang; Remi Jacob Lay; Stuart K. Roberts; Sunita ChauhanAccurate and efficient prediction of soft tissue temperatures is essential to computerassisted treatment systems for thermal ablation. It can be used to predict tissue temperatures and ablation volumes for personalised treatment planning and imageguided intervention. Numerically, it requires full nonlinear modelling of the coupled computational bioheat transfer and biomechanics, and efficient solution

Topology Optimization through Differentiable Finite Element Solver arXiv.cs.CE Pub Date : 20200920
Liang Chen; Herman M. H. ShenIn this paper, a topology optimization framework utilizing automatic differentiation is presented as an efficient way for solving 2D densitybased topology optimization problem by calculating gradients through the fully differentiable finite element solver. The optimization framework with the differentiable physics solver is proposed and tested on several classical topology optimization examples. The

IndustryRelevant Implicit LargeEddy Simulation of a HighPerformance Road Car via Spectral/hp Element Methods arXiv.cs.CE Pub Date : 20200918
Gianmarco Mengaldo; David Moxey; Michael Turner; Rodrigo C. Moura; Ayad Jassim; Mark Taylor; Joaquim Peiro; Spencer J. SherwinWe present a successful deployment of highfidelity LargeEddy Simulation (LES) technologies based on spectral/hp element methods to industrial flow problems, which are characterized by high Reynolds numbers and complex geometries. In particular, we describe the numerical methods, software development and steps that were required to perform the implicit LES of a real automotive car, namely the Elemental

Analysis of tunnel failure characteristics under multiple explosion loads based on persistent homologybased machine learning arXiv.cs.CE Pub Date : 20200919
Shengdong Zhang; Shihui You; Longfei Chen; Xiaofei LiuThe study of tunnel failure characteristics under the load of external explosion source is an important problem in tunnel design and protection, in particular, it is of great significance to construct an intelligent topological feature description of the tunnel failure process. The failure characteristics of tunnels under explosive loading are described by using discrete element method and persistent

A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branchheight Based on Artificial Neural Networks and Integer Programming arXiv.cs.CE Pub Date : 20200921
Naveed Ahmed Azam; Jianshen Zhu; Yanming Sun; Yu Shi; Aleksandar Shurbevski; Liang Zhao; Hiroshi Nagamochi; Tatsuya AkutsuAnalysis of chemical graphs is a major research topic in computational molecular biology due to its potential applications to drug design. One approach is inverse quantitative structure activity/property relationship (inverse QSAR/QSPR) analysis, which is to infer chemical structures from given chemical activities/properties. Recently, a framework has been proposed for inverse QSAR/QSPR using artificial

A new fronttracking Lagrangian model for the modeling of dynamic and postdynamic recrystallization arXiv.cs.CE Pub Date : 20200917
Sebastian Florez; Karen Alvarado; Marc BernackiA new method for the simulation of evolving multidomains problems has been introduced in previous works (RealIMotion), Florez et al. (2020) and further developed in parallel in the context of isotropic Grain Growth (GG) with no consideration for the effects of the Stored Energy (SE) due to dislocations. The methodology consists in a new fronttracking approach where one of the originality is that

A numerical approach for hybrid reliability analysis of structures under mixed uncertainties using the uncertainty theory arXiv.cs.CE Pub Date : 20200917
Lei ZhangThis paper presents a novel numerical method for the hybrid reliability analysis by using the uncertainty theory. Aleatory uncertainty and epistemic uncertainty are considered simultaneously in this method. Epistemic uncertainty is characterized by the uncertainty theory, and the effect of epistemic uncertainty is quantified by the subadditive uncertain measure. Then, under the framework of the chance

Broadband FiniteElement Impedance Computation for Parasitic Extraction arXiv.cs.CE Pub Date : 20200917
Jonathan Stysch; Andreas Klaedtke; Herbert De GersemParasitic extraction is a powerful tool in the design process of electromechanical devices, specifically as part of workflows that ensure electromagnetic compatibility. A novel scheme to extract impedances from CAD device models, suitable for a finite element implementation, is derived from Maxwell's equations in differential form. It provides a foundation for parasitic extraction across a broad frequency

A micropolar peridynamics model with nonunified horizon for damage of solids with different nonlocal effects arXiv.cs.CE Pub Date : 20200917
Yiming Zhang; Xueqing Yang; Xiaoying ZhuangMost peridynamics models adopt regular point distribution and unified horizon, limiting their flexibility and engineering applications. In this work, a micropolar peridynamics approach with nonunified horizon (NHPD) is proposed. This approach is implemented in a conventional finite element framework, using elementbased discretization. By modifying the dual horizon approach into the preprocessing

Variational phasefield continuum model uncovers adhesive wear mechanisms in asperity junctions arXiv.cs.CE Pub Date : 20200917
Sylvain Collet; JeanFrançois Molinari; Stella BrachWear is well known for causing material loss in a sliding interface. Available macroscopic approaches are bound to empirical fitting parameters, which range several orders of magnitude. Major advances in tribology have recently been achieved via Molecular Dynamics, although its use is strongly limited by computational cost. Here, we propose a study of the physical processes that lead to wear at the

Computational tool to study high dimensional dynamic in NMM arXiv.cs.CE Pub Date : 20200916
A. GonzálezMitjans; D. PazLinares; A. ArecesGonzalez; ML. BringasVega; P. A ValdésSosaNeuroscience has shown great progress in recent years. Several of the theoretical bases have arisen from the examination of dynamic systems, using Neural Mass Models (NMMs). Due to the largescale brain dynamics of NMMs and the difficulty of studying nonlinear systems, the local linearization approach to discretize the state equation was used via an algebraic formulation, as it intervenes favorably

A timespectral Stokes solver for simulation of timeperiodic flows in complex geometries arXiv.cs.CE Pub Date : 20200911
Chenwei Meng; Mahdi EsmailySimulation of unsteady creeping flows in complex geometries has traditionally required the use of a timestepping procedure, which is typically costly and unscalable. To reduce the cost and allow for computations at much larger scales, we propose an alternative approach that is formulated based on the unsteady Stokes equation expressed in the timespectral domain. This transformation results in a boundary

User Manual for the SU2 EQUiPS Module: Enabling Quantification of Uncertainty in Physics Simulations arXiv.cs.CE Pub Date : 20200913
Jayant MukhopadhayaThis document serves as the manual for using the EQUiPS (Enabling Quantification of Uncertainty in Physics Simulations) module in SU2. The EQUiPS module uses the Eigenspace Perturbation methodology to provide interval bounds on Quantities of Interest (QoIs) that capture epistemic uncertainties arising from assumptions made in RANS turbulence models. This has been implemented and tested in SU2 for a

DataDriven Optimization Approach for Inverse Problems: Application to Turbulent MixedConvection Flows arXiv.cs.CE Pub Date : 20200910
M. Oulghelou; C. Beghein; C. AlleryOptimal control of turbulent mixedconvection flows has attracted considerable attention from researchers. Numerical algorithms such as Genetic Algorithms (GAs) are powerful tools that allow to perform global optimization. These algorithms are particularly of great interest in complex optimization problems where cost functionals may lack smoothness and regularity. In turbulent flow optimization, the

MOPaDGAN: Reparameterizing Engineering Designs for Augmented Multiobjective Optimization arXiv.cs.CE Pub Date : 20200915
Wei Chen; Faez AhmedMultiobjective optimization is key to solving many Engineering Design problems, where design parameters are optimized for several performance indicators. However, optimization results are highly dependent on how the designs are parameterized. Researchers have shown that deep generative models can learn compact design representations, providing a new way of parameterizing designs to achieve faster

The impact of social influence in Australian realestate: market forecasting with a spatial agentbased model arXiv.cs.CE Pub Date : 20200915
Benjamin Patrick Evans; Kirill Glavatskiy; Michael S. Harré; Mikhail ProkopenkoHousing markets are inherently spatial, yet many existing models fail to capture this spatial dimension. Here we introduce a new graphbased approach for incorporating a spatial component in a largescale urban housing agentbased model (ABM). The model explicitly captures several social and economic factors that influence the agents' decisionmaking behaviour (such as fear of missing out, their trend

Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the PeckingOrder arXiv.cs.CE Pub Date : 20200915
Michael Rollins; Dave CliffThis paper presents novel results generated from a new simulation model of a contemporary financial market, that cast serious doubt on the previously widely accepted view of the relative performance of various wellknown publicdomain automatedtrading algorithms. Various publicdomain trading algorithms have been proposed over the past 25 years in a kind of armsrace, where each new trading algorithm

Interfacing biology, category theory and mathematical statistics arXiv.cs.CE Pub Date : 20200915
Dominique PastorIMT Atlantique, LabSTICC, Université de BretagneLoire; Erwan BeurierIMT Atlantique, LabSTICC, Université de BretagneLoire; Andrée EhresmannFaculté des Sciences, Mathématiques, LAMFA, Université de Picardie Jules Verne; Roger WaldeckIMT Atlantique, LEGO, Université de BretagneLoireMotivated by the concept of degeneracy in biology (Edelman, Gally 2001), we establish a first connection between the Multiplicity Principle (Ehresmann, Vanbremeersch 2007) and mathematical statistics. Specifically, we exhibit two families of statistical tests that satisfy this principle to achieve the detection of a signal in noise.

A novel combination of theoretical analysis and datadriven method for reconstruction of structural defects arXiv.cs.CE Pub Date : 20200914
Qi Li; Yihui Da; Yinghong Zhang; Bin Wang; Dianzi Liu; Zhenghua QianUltrasonic guided wave technology has played a significant role in the field of nondestructive testing as it employs acoustic waves that have advantages of high propagation efficiency and low energy consumption during the inspect process. However, theoretical solutions to guided wave scattering problems using assumptions such as Born approximation, have led to the poor quality of the reconstructed

Lattice Boltzmann Method for wave propagation in elastic solids with a regular lattice: Theoretical analysis and validation arXiv.cs.CE Pub Date : 20200910
Maxime Escande; Praveen Kumar Kolluru; Louis Marie Cléon; Pierre SagautThe von Neumann stability analysis along with a ChapmanEnskog analysis is proposed for a singlerelaxationtime lattice Boltzmann Method (LBM) for wave propagation in isotropic linear elastic solids, using a regular D2Q9 lattice. Different boundary conditions are considered: periodic, free surface, rigid interface. An original absorbing layer model is proposed to prevent spurious wave reflection at

A blockcoupled Finite Volume methodology for problems of large strain and large displacement arXiv.cs.CE Pub Date : 20200909
L. R. Azevedo; P. Cardiff; F. J. GalindoRosales; M. SchaferA nonlinear blockcoupled Finite Volume methodology is developed for large displacement and large strain regime. The new methodology uses the same normal and tangential face derivative discretisations found in the original fully coupled cellcentred Finite Volume solution methodology for linear elasticity, meaning that existing blockcoupled implementations may easily be extended to include finite

On topology optimization of designdependent pressureloaded 3D structures and compliant mechanisms arXiv.cs.CE Pub Date : 20200912
Prabhat Kumar; Matthijs LangelaarThis paper presents a densitybased topology optimization method for designing 3D compliant mechanisms and loadbearing structures with designdependent pressure loading. Instead of interfacetracking techniques, the Darcy law in conjunction with a drainage term is employed to obtain pressure field as a function of the design vector. To ensure continuous transition of pressure loads as the design evolves

Identifying Greybox Thermal Models with Bayesian Neural Networks arXiv.cs.CE Pub Date : 20200913
Md Monir Hossain; Tianyu Zhang; Omid ArdakanianSmart thermostats are one of the most prevalent home automation products. They learn occupant preferences and schedules, and utilize an accurate thermal model to reduce the energy use of heating and cooling equipment while maintaining the temperature for maximum comfort. Despite the importance of having an accurate thermal model for the operation of smart thermostats, fast and reliable identification

Hybridisable discontinuous Galerkin formulation of compressible flows arXiv.cs.CE Pub Date : 20200910
Jordi VilaPérez; Matteo Giacomini; Ruben Sevilla; Antonio HuertaThis work presents a review of highorder hybridisable discontinuous Galerkin (HDG) methods in the context of compressible flows. Moreover, an original unified framework for the derivation of Riemann solvers in hybridised formulations is proposed. This framework includes, for the first time in an HDG context, the HLL and HLLEM Riemann solvers as well as the traditional LaxFriedrichs and Roe solvers

Allocation of locally generated electricity in renewable energy communities arXiv.cs.CE Pub Date : 20200909
Miguel Manuel de Villena; Sébastien Mathieu; Eric Vermeulen; Damien ErnstThis paper introduces a methodology to perform an expost allocation of locally generated electricity among the members of a renewable energy community. Such an expost allocation takes place in a settlement phase where the financial exchanges of the community are based on the production and consumption profiles of each member. The proposed methodology consists of an optimisation framework which (i)

A weakly compressible hybridizable discontinuous Galerkin formulation for fluidstructure interaction problems arXiv.cs.CE Pub Date : 20200909
Andrea La Spina; Martin Kronbichler; Matteo Giacomini; Wolfgang A. Wall; Antonio HuertaA scheme for the solution of fluidstructure interaction (FSI) problems with weakly compressible flows is proposed in this work. A novel hybridizable discontinuous Galerkin (HDG) method is derived for the discretization of the fluid equations, while the standard continuous Galerkin (CG) approach is adopted for the structural problem. The chosen HDG solver combines robustness of discontinuous Galerkin

A novel highly efficient Lagrangian model for massively multidomain simulations: parallel context arXiv.cs.CE Pub Date : 20200909
Sebastian Florez; Julien Fausty; Karen Alvarado; Brayan Murgas; Marc BernackiA new method for the simulation of evolving multidomains problems has been introduced in a previous work (RealIMotion), Florez et al. (2020). In this article further developments of the model will be presented. The main focus here is a robust parallel implementation using a distributedmemory approach with the Message Passing Interface (MPI) library OpenMPI. The original 2D sequential methodology

Performance Analysis of FEM Solvers on Practical Electromagnetic Problems arXiv.cs.CE Pub Date : 20200904
Gergely Máté Kiss; Jan Kaska; Roberto André Henrique de Oliveira; Olena Rubanenko; Balázs TóthThe paper presents a comparative analysis of different commercial and academic software. The comparison aims to examine how the integrated adaptive grid refinement methodologies can deal with challenging, electromagneticfield related problems. For this comparison, two benchmark problems were examined in the paper. The first example is a solution of an Lshape domain like test problem, which has a

Realtime Bayesian personalization via a learnable brain tumor growth model arXiv.cs.CE Pub Date : 20200909
Ivan Ezhov; Tudor Mot; Suprosanna Shit; Jana Lipkova; Johannes C. Paetzold; Florian Kofler; Fernando Navarro; Marie Metz; Benedikt Wiestler; Bjoern MenzeModeling of brain tumor dynamics has the potential to advance therapeutic planning. Current modeling approaches resort to numerical solvers that simulate the tumor progression according to a given differential equation. Using highlyefficient numerical solvers, a single forward simulation takes up to a few minutes of compute. At the same time, clinical applications of the tumor modeling often imply

Using Spectral Submanifolds for Optimal Mode Selection in Model Reduction arXiv.cs.CE Pub Date : 20200909
Gergely Buza; Shobhit Jain; George HallerModel reduction of large nonlinear systems often involves the projection of the governing equations onto linear subspaces spanned by carefullyselected modes. The criteria to select the modes relevant for reduction are usually problemspecific and heuristic. In this work, we propose a rigorous modeselection criterion based on the recent theory of Spectral Submanifolds (SSM), which facilitates a reliable

Automatic featurepreserving size field for 3D mesh generation arXiv.cs.CE Pub Date : 20200904
Arthur Bawin; François Henrotte; JeanFrançois RemacleThis paper presents a methodology aiming at easing considerably the generation of highquality meshes for complex 3D domains. We show that the whole mesh generation process can be controlled with only five parameters to generate in one stroke quality meshes for arbitrary geometries. The main idea is to build a meshsize field $h(x)$ taking local features of the geometry, such as curvatures, into account

Development and comparison of spectral algorithms for numerical modeling of the quasistatic mechanical behavior of inhomogeneous materials arXiv.cs.CE Pub Date : 20200904
M. Khorrami; J. R. Mianroodi; P. Shanthraj; B. SvendsenIn the current work, a number of algorithms are developed and compared for the numerical solution of periodic (quasistatic) linear elastic mechanical boundaryvalue problems (BVPs) based on two different discretizations of Fourier series. The first is standard and based on the trapezoidal approximation of the Fourier mode integral, resulting in trapezoidal discretization (TD) of the truncated Fourier

TaBooN  Boolean Network Synthesis Based on Tabu Search arXiv.cs.CE Pub Date : 20200908
Sara Sadat Aghamiri; Franck DelaplaceRecent developments in Omicstechnologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of

An integrative smoothed particle hydrodynamics framework for modeling cardiac function arXiv.cs.CE Pub Date : 20200904
Chi Zhang; Jianhang Wang; Massoud Rezavand; Dong Wu; Xiangyu HuMathematical modeling of cardiac function can provide augmented simulationbased diagnosis tool for complementing and extending human understanding of cardiac diseases which represent the most common cause of worldwide death. As the realistic startingpoint for developing an unified meshless approach for total heart modeling, herein we propose an integrative smoothed particle hydrodynamics (SPH) framework

Improving Maritime Traffic Emission Estimations on Missing Data with CRBMs arXiv.cs.CE Pub Date : 20200907
Alberto GutierrezTorre; Josep Ll. Berral; David Buchaca; Marc Guevara; Albert Soret; David CarreraMaritime traffic emissions are a major concern to governments as they heavily impact the Air Quality in coastal cities. Ships use the Automatic Identification System (AIS) to continuously report position and speed among other features, and therefore this data is suitable to be used to estimate emissions, if it is combined with engine data. However, important ship features are often inaccurate or missing

A nested genetic algorithm strategy for the optimal plastic design of frames arXiv.cs.CE Pub Date : 20200904
A. Greco; F. Cannizzaro; R. Bruno; A. PluchinoAn innovative strategy for the optimal design of planar frames able to resist to seismic excitations is here proposed. The procedure is based on genetic algorithms (GA) which are performed according to a nested structure suitable to be implemented in parallel computing on several devices. In particular, this solution foresees two nested genetic algorithms. The first one, named "External GA", seeks

AIMx: An Extended Adaptive Integral Method for the Fast Electromagnetic Modeling of Complex Structures arXiv.cs.CE Pub Date : 20200901
Shashwat Sharma; Piero TriverioSurface integral equation (SIE) methods are of great interest for the efficient electromagnetic modeling of various devices, from integrated circuits to antenna arrays. Existing acceleration algorithms for SIEs, such as the adaptive integral method (AIM), enable the fast approximation of interactions between wellseparated mesh elements. Nearby interactions involve the singularity of the kernel, and

Separated response surfaces for flows in parametrised domains: comparison of a priori and a posteriori PGD algorithms arXiv.cs.CE Pub Date : 20200904
Matteo Giacomini; Luca Borchini; Ruben Sevilla; Antonio HuertaReduced order models (ROM) are commonly employed to solve parametric problems and to devise inexpensive response surfaces to evaluate quantities of interest in realtime. There are many families of ROMs in the literature and choosing among them is not always a trivial task. This work presents a comparison of the performance of a priori and a posteriori proper generalised decomposition (PGD) algorithms

FrequencyDependent Material Motion Benchmarks for Radiative Transfer arXiv.cs.CE Pub Date : 20200803
Ryan G. McClarren; N. A. GentileWe present a general solution for the radiation intensity in front of a purely absorbing slab moving toward an observer at constant speed and with a constant temperature. The solution is obtained by integrating the labframe radiation transport equation through the slab to the observer. We present comparisons between our benchmark and results from the Kull simulation code for an aluminum slab moving

Accelerating engineering design by automatic selection of simulation cases through PoolBased Active Learning arXiv.cs.CE Pub Date : 20200903
José Hugo C. Gaspar Elsas; Nicholas A. G. Casaprima; Ivan F. M. MenezesA common workflow for many engineering design problems requires the evaluation of the design system to be investigated under a range of conditions. These conditions usually involve a combination of several parameters. To perform a complete evaluation of a single candidate configuration, it may be necessary to perform hundreds to thousands of simulations. This can be computationally very expensive,

An Expedient Approach to FDTDbased Modeling of Finite Periodic Structures arXiv.cs.CE Pub Date : 20200831
Aaron J. Kogon; Costas D. SarrisThis paper proposes an efficient FDTD technique for determining electromagnetic fields interacting with a finitesized 2D and 3D periodic structures. The technique combines periodic boundary conditionsmodelling fields away from the edges of the structurewith independent simulations of fields near the edges of the structure. It is shown that this algorithm efficiently determines the size of a

Towards Earnings Call and Stock Price Movement arXiv.cs.CE Pub Date : 20200823
Zhiqiang Ma; Grace Bang; Chong Wang; Xiaomo LiuEarnings calls are hosted by management of public companies to discuss the company's financial performance with analysts and investors. Information disclosed during an earnings call is an essential source of data for analysts and investors to make investment decisions. Thus, we leverage earnings call transcripts to predict future stock price dynamics. We propose to model the language in transcripts

Industrial scale large eddy simulations (LES) with adaptive octree meshes using immersogeometric analysis arXiv.cs.CE Pub Date : 20200828
Kumar Saurabh; Boshun Gao; Milinda Fernando; Songzhe Xu; Biswajit Khara; Makrand A. Khanwale; MingChen Hsu; Adarsh Krishnamurthy; Hari Sundar; Baskar GanapathysubramanianWe present a variant of the immersed boundary method integrated with octree meshes for highly efficient and accurate LargeEddy Simulations (LES) of flows around complex geometries. We demonstrate the scalability of the proposed method up to $\mathcal{O}(32K)$ processors. This is achieved by (a) rapid inout tests; (b) adaptive quadrature for an accurate evaluation of forces; (c) tensorized evaluation

Accelerated reactive transport simulations in heterogeneous porous medium using Reaktoro and Firedrake arXiv.cs.CE Pub Date : 20200817
Svetlana Kyas; Diego Volpatto; Martin O. Saar; Allan M. M. LealGeochemical reaction calculations in reactive transport modeling are costly in general. They become more expensive the more complex is the chemical system and the activity models used to describe the nonideal thermodynamic behavior of its phases. Accounting for many aqueous species, gases, and minerals also contributes to more expensive computations. This work investigates the performance of the ondemand

TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments arXiv.cs.CE Pub Date : 20200826
Adam Yonge; M. Ross Kunz; Rakesh Batchu; Zongtang Fang; Tobin Issac; Rebecca Fushimi; Andrew J. MedfordAn opensource, Pythonbased Temporal Analysis of Products (TAP) reactor simulation and processing program is introduced. TAPsolver utilizes algorithmic differentiation for the calculation of highly accurate derivatives, which are used to perform sensitivity analyses and PDEconstrained optimization. The tool supports constraints to ensure thermodynamic consistency, which can lead to more accurate

Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physicsinformed neural networks arXiv.cs.CE Pub Date : 20200728
Qiming Zhu; Zeliang Liu; Jinhui YanThe recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling. However, the success of conventional machine learning tools in data science is primarily attributed to the unprecedented large amount of labeled datasets (big data), which can be either obtained by experiments or firstprinciple

A Newton Solver for Micromorphic Computational Homogenization Enabling Multiscale Buckling Analysis of PatternTransforming Metamaterials arXiv.cs.CE Pub Date : 20200831
S. E. H. M. van Bree; O. Rokoš; R. H. J. Peerlings; M. Doškář; M. G. D. GeersMechanical metamaterials feature engineered microstructures designed to exhibit exotic, and often counterintuitive, effective behaviour. Such a behaviour is often achieved through instabilityinduced transformations of the underlying periodic microstructure into one or multiple patterning modes. Due to a strong kinematic coupling of individual repeating microstructural cells, nonlocal behaviour and

Momentumbased Accelerated Mirror Descent Stochastic Approximation for Robust Topology Optimization under Stochastic Loads arXiv.cs.CE Pub Date : 20200830
Weichen Li; Xiaojia Shelly ZhangRobust topology optimization (RTO) improves the robustness of designs with respect to random sources in realworld structures, yet an accurate sensitivity analysis requires the solution of many systems of equations at each optimization step, leading to a high computational cost. To open up the full potential of RTO under a variety of random sources, this paper presents a momentumbased accelerated

A fractional stochastic theory for interfacial polarization of cell aggregates arXiv.cs.CE Pub Date : 20200825
Pouria A. Mistani; Samira Pakravan; Frederic G. GibouWe present a theoretical framework to model the electric response of cell aggregates. We establish a coarse representation for each cell as a combination of membrane and cytoplasm dipole moments. Then we compute the effective conductivity of the resulting system, and thereafter derive a FokkerPlanck partial differential equation that captures the timedependent evolution of the distribution of induced

An immersed phase field fracture model for fluidinfiltrating porous media with evolving BeaversJosephSaffman condition arXiv.cs.CE Pub Date : 20200811
Hyoung Suk Suh; WaiChing SunThis study presents a phase field model for brittle fracture in fluidinfiltrating vuggy porous media. While the stateoftheart in hydraulic phase field fracture considers Darcian fracture flow with enhanced permeability along the crack, in this study, the phase field not only acts as a damage variable that provides diffuse representation of cracks or cavities, but also acts as an indicator function

FractionalOrder Structural Stability: Formulation and Application to the Critical Load of Slender Structures arXiv.cs.CE Pub Date : 20200815
Sai Sidhardh; Sansit Patnaik; Fabio SemperlottiThis study presents the framework to perform a stability analysis of nonlocal solids whose response is formulated according to the fractionalorder continuum theory. In this formulation, space fractionalorder operators are used to capture the nonlocal response of the medium by introducing nonlocal kinematic relations. First, we use the geometrically nonlinear fractionalorder kinematic relations within

Highly scalable numerical simulation of coupled reactiondiffusion systems with moving interfaces arXiv.cs.CE Pub Date : 20200825
Mojtaba Barzegari; Liesbet GerisA combination of reactiondiffusion models with movingboundary problems yields a system in which the diffusion (spreading and penetration) and reaction (transformation) evolve the system's state and geometry over time. These systems can be used in a wide range of engineering applications. In this study, as an example of such a system, the degradation of metallic materials is investigated. A mathematical

Introducing students to research codes: A short course on solving partial differential equations in Python arXiv.cs.CE Pub Date : 20200825
Pavan Inguva; Vijesh J. Bhute; Thomas N. H. Cheng; Pierre J. WalkerRecent releases of opensource research codes and solvers for numerically solving partial differential equations in Python present a great opportunity for educators to integrate these codes into the classroom in a variety of ways. The ease with which a problem can be implemented and solved using these codes reduce the barrier to entry for users. We demonstrate how one of these codes,FiPy, can be introduced

VariableOrder Fracture Mechanics and its Application to Dynamic Fracture arXiv.cs.CE Pub Date : 20200816
Sansit Patnaik; Fabio SemperlottiThis study presents the formulation, the numerical solution, and the validation of a theoretical framework based on the concept of variableorder mechanics and capable of modeling dynamic fracture in brittle and quasibrittle solids. More specifically, the reformulation of the elastodynamic problem via variable and fractional order operators enables a unique and extremely powerful approach to model

Hierarchical Deep Learning of Multiscale Differential Equation TimeSteppers arXiv.cs.CE Pub Date : 20200822
Yuying Liu; J. Nathan Kutz; Steven L. BruntonNonlinear differential equations rarely admit closedform solutions, thus requiring numerical timestepping algorithms to approximate solutions. Further, many systems characterized by multiscale physics exhibit dynamics over a vast range of timescales, making numerical integration computationally expensive due to numerical stiffness. In this work, we develop a hierarchy of deep neural network timesteppers

On the treatment of boundary conditions for bondbased peridynamic models arXiv.cs.CE Pub Date : 20200822
Serge Prudhomme; Patrick DiehlIn this paper, we propose two approaches to apply boundary conditions for bondbased peridynamic models. There has been in recent years a renewed interest in the class of socalled nonlocal models, which include peridynamic models, for the simulation of structural mechanics problems as an alternative approach to classical local continuum models. However, a major issue, which is often disregarded when

Variational Autoencoder for AntiCancer Drug Response Prediction arXiv.cs.CE Pub Date : 20200822
Jiaqing Xie; Hongyuan Dong; Zhi Jing; Dexin RenThere is remarkable progress in identifying the perplexity of genomic landscape of cancer over the past two decades, providing us with huge information resources on anticancer drugs. In this work, we seek to predict the response and efficacy for different anticancer drugs on breast cancer and generalize it on pancancer by using deep learning models. In order to get the result without dealing with

A Principled Approach to Design Using High Fidelity FluidStructure Interaction Simulations arXiv.cs.CE Pub Date : 20200821
Wensi Wu; Christophe Bonneville; Christopher J. EarlsA high fidelity fluidstructure interaction simulation may require many days to run, on hundreds of cores. This poses a serious burden, both in terms of time and economic considerations, when repetitions of such simulations may be required (e.g. for the purpose of design optimization). In this paper we present strategies based on (constrained) Bayesian optimization (BO) to alleviate this burden. BO

A Blockchain Transaction Graph based Machine Learning Method for Bitcoin Price Prediction arXiv.cs.CE Pub Date : 20200821
Xiao Li; Weili WuBitcoin, as one of the most popular cryptocurrency, is recently attracting much attention of investors. Bitcoin price prediction task is consequently a rising academic topic for providing valuable insights and suggestions. Existing bitcoin prediction works mostly base on trivial feature engineering, that manually designs features or factors from multiple areas, including Bticoin Blockchain information