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  • A new Hodge operator in Discrete Exterior Calculus. Application to fluid mechanics
    arXiv.cs.CE Pub Date : 2020-06-29
    Rama Ayoub; Aziz Hamdouni; Dina Razafindralandy

    This article introduces a new and general construction of discrete Hodge operator in the context of Discrete Exterior Calculus (DEC). This discrete Hodge operator enables to circumvent the well-centeredness limitation on the mesh with the popular diagonal Hodge. It allows a dual mesh based on any interior point, such as the incenter or the barycenter. It opens the way towards mesh-optimized discrete

    更新日期:2020-07-01
  • On Dynamic Substructuring of Systems with Localised Nonlinearities
    arXiv.cs.CE Pub Date : 2020-06-30
    Thomas Simpson; Dimitrios Giagopoulos; Vasilis Dertimanis; Eleni Chatzi

    Dynamic substructuring (DS) methods encompass a range of techniques to decompose large structural systems into multiple coupled subsystems. This decomposition has the principle benefit of reducing computational time for dynamic simulation of the system. In this context, DS methods may form an essential component of hybrid simulation, wherein they can be used to couple physical and numerical substructures

    更新日期:2020-07-01
  • Spiral capacitor calculation using FEniCS
    arXiv.cs.CE Pub Date : 2020-06-26
    Slava Andrejev

    The paper shows how to optimize a water level sensor consisting of a cylinder with spiraling metal stripes on the side, using a powerful Python library FEniCS. It is shown how to reduce a 3D Laplace equation to a 2D, using a spiraling coordinate system; how to specify the correct boundary conditions for an open region; how to convert the partial differential equation to a variational form for FEniCS;

    更新日期:2020-07-01
  • On the Potenital of Dynamic Substructuring Methods for Model Updating
    arXiv.cs.CE Pub Date : 2020-06-30
    Thomas Simpson; Vasilis Dertimanis; Costas Papadimitriou; Eleni Chatzi

    While purely data-driven assessment is feasible for the first levels of the Structural Health Monitoring (SHM) process, namely damage detection and arguably damage localization, this does not hold true for more advanced processes. The tasks of damage quantification and eventually residual life prognosis are invariably linked to availability of a representation of the system, which bears physical connotation

    更新日期:2020-07-01
  • A Bayesian regularization-backpropagation neural network model for peeling computations
    arXiv.cs.CE Pub Date : 2020-06-29
    Saipraneeth Gouravaraju; Jyotindra Narayan; Roger A. Sauer; Sachin Singh Gautam

    Bayesian regularization-backpropagation neural network (BR-BPNN), a machine learning algorithm, is employed to predict some aspects of the gecko spatula peeling such as the variation of the maximum normal and tangential pull-off forces and the resultant force angle at detachment with the peeling angle. The input data is taken from finite element (FE) peeling results. The neural network is trained with

    更新日期:2020-07-01
  • Tusas: A fully implicit parallel approach for coupled nonlinear equations
    arXiv.cs.CE Pub Date : 2020-06-27
    Supriyo Ghosh; Christopher K. Newman; Marianne M. Francois

    We develop a fully-coupled, fully-implicit approach for phase-field modeling of solidification in metals and alloys. Predictive simulation of solidification in pure metals and metal alloys remains a significant challenge in the field of materials science, as microstructure formation during the solidification process plays a critical role in the properties and performance of the solid material. Our

    更新日期:2020-07-01
  • Dynamic Hedging using Generated Genetic Programming Implied Volatility Models
    arXiv.cs.CE Pub Date : 2020-06-29
    Fathi Abid; Wafa Abdelmalek; Sana Ben Hamida

    The purpose of this paper is to improve the accuracy of dynamic hedging using implied volatilities generated by genetic programming. Using real data from S&P500 index options, the genetic programming's ability to forecast Black and Scholes implied volatility is compared between static and dynamic training-subset selection methods. The performance of the best generated GP implied volatilities is tested

    更新日期:2020-07-01
  • Parametric Modeling of EEG by Mono-Component Non-Stationary Signal
    arXiv.cs.CE Pub Date : 2020-06-29
    Pradip Sircar; Rakesh Kumar Sharma

    In this paper, we propose a novel approach for parametric modeling of electroencephalographic (EEG) signals. It is demonstrated that the EEG signal is a mono-component non-stationary signal whose amplitude and phase (frequency) can be expressed as functions of time. We present detailed strategy for estimation of the parameters of the proposed model with high accuracy. Simulation study illustrates the

    更新日期:2020-06-30
  • A new level set-finite element formulation for anisotropic grain boundary migration
    arXiv.cs.CE Pub Date : 2020-06-28
    J. Fausty; B. Murgas; S. Florez; N. Bozzolo; M. Bernacki

    Grain growth in polycrystals is one of the principal mechanisms that take place during heat treatment of metallic components. This work treats an aspect of the anisotropic grain growth problem. By applying the first principles of thermodynamics and mechanics, an expression for the velocity field of a migrating grain boundary with an inclination dependent energy density is expressed. This result is

    更新日期:2020-06-30
  • Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems
    arXiv.cs.CE Pub Date : 2020-06-27
    Liwei Wang; Yu-Chin Chan; Faez Ahmed; Zhao Liu; Ping Zhu; Wei Chen

    The inverse design of metamaterials is difficult due to a high-dimensional topological design space and presence of multiple local optima. Computational cost is even more demanding for design of multiscale metamaterial systems with aperiodic microstructures and spatially-varying or functionally gradient properties. Despite the growing interest in applying data-driven methods to address this hurdle

    更新日期:2020-06-30
  • Data-Driven Topology Optimization with Multiclass Microstructures using Latent Variable Gaussian Process
    arXiv.cs.CE Pub Date : 2020-06-27
    Liwei Wang; Siyu Tao; Ping Zhu; Wei Chen

    The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or distance measure between different

    更新日期:2020-06-30
  • A spectral collocation method for the Landau equation in plasma physics
    arXiv.cs.CE Pub Date : 2020-06-29
    Francis FilbetUT3

    In this paper we present a spectral collocation method for the fast evaluation of the Landau collision operator for plasma physics, which allows us to obtain spectrally accurate numerical solutions. The method is inspired by the seminal work [36], but it is specifically designed for Coulombian interactions, taking into account the particular structure of the operator. It allows us to reduce the number

    更新日期:2020-06-30
  • A high-order well-balanced positivity-preserving moving mesh DG method for the shallow water equations with non-flat bottom topography
    arXiv.cs.CE Pub Date : 2020-06-26
    Min Zhang; Weizhang Huang; Jianxian Qiu

    A rezoning-type adaptive moving mesh discontinuous Galerkin method is proposed for the numerical solution of the shallow water equations with non-flat bottom topography. The well-balance property is crucial to the simulation of perturbation waves over the lake-at-rest steady state such as waves on a lake or tsunami waves in the deep ocean. To ensure the well-balance and positivity-preserving properties

    更新日期:2020-06-30
  • Extracting Non-Gaussian Governing Laws from Data on Mean Exit Time
    arXiv.cs.CE Pub Date : 2020-06-24
    Yanxia Zhang; Jinqiao Duan; Yanfei Jin; Yang Li

    Motivated by the existing difficulties in establishing mathematical models and in observing the system state time series for some complex systems, especially for those driven by non-Gaussian Levy motion, we devise a method for extracting non-Gaussian governing laws with observations only on mean exit time. It is feasible to observe mean exit time for certain complex systems. With the observations,

    更新日期:2020-06-29
  • An unsupervised deep learning approach in solving partial-integro differential equations
    arXiv.cs.CE Pub Date : 2020-06-26
    Ali Hirsa; Weilong Fu

    We investigate solving partial integro-differential equations (PIDEs) using unsupervised deep learning in this paper. To price options, assuming underlying processes follow \levy processes, we require to solve PIDEs. In supervised deep learning, pre-calculated labels are used to train neural networks to fit the solution of the PIDE. In an unsupervised deep learning, neural networks are employed as

    更新日期:2020-06-29
  • Long-term Health Index Prediction for Power Asset Classes Based on Sequence Learning
    arXiv.cs.CE Pub Date : 2020-06-25
    Ming Dong; Wenyuan Li; Alex Nassif

    Utility companies have widely adopted the concept of health index to describe asset health statuses and choose proper asset management actions. The existing application and research works have been focused on determining the current or near-future asset health index based on the current condition data. For long-term preventative asset management, it is highly desirable to predict asset health indices

    更新日期:2020-06-26
  • Accelerating MRI Reconstruction on TPUs
    arXiv.cs.CE Pub Date : 2020-06-24
    Tianjian Lu; Thibault Marin; Yue Zhuo; Yi-Fan Chen; Chao Ma

    The advanced magnetic resonance (MR) image reconstructions such as the compressed sensing and subspace-based imaging are considered as large-scale, iterative, optimization problems. Given the large number of reconstructions required by the practical clinical usage, the computation time of these advanced reconstruction methods is often unacceptable. In this work, we propose using Google's Tensor Processing

    更新日期:2020-06-26
  • Level set based eXtended finite element modelling of the response of fibrous networks under hygroscopic swelling
    arXiv.cs.CE Pub Date : 2020-06-23
    P. Samantray; R. H. J. Peerlings; E. Bosco; M. G. D. Geers; T. J. Massart; O. Rokoš

    Materials like paper, consisting of a network of natural fibres, exposed to variations in moisture, undergo changes in geometrical and mechanical properties. This behaviour is particularly important for understanding the hygro-mechanical response of sheets of paper in applications like digital printing. A two-dimensional microstructural model of a fibrous network is therefore developed to upscale the

    更新日期:2020-06-26
  • Mesh deformation techniques in fluid-structure interaction: robustness, accumulated distortion and computational efficiency
    arXiv.cs.CE Pub Date : 2020-06-19
    Alexander Shamanskiy; Bernd Simeon

    An important ingredient of any moving-mesh method for fluid-structure interaction (FSI) problems is the mesh deformation technique (MDT) used to adapt the computational mesh in the moving fluid domain. An ideal technique is computationally inexpensive, can handle large mesh deformations without inverting mesh elements and can sustain an FSI simulation for extensive periods ot time without irreversibly

    更新日期:2020-06-26
  • Accelerating Training in Artificial Neural Networks with Dynamic Mode Decomposition
    arXiv.cs.CE Pub Date : 2020-06-18
    Mauricio E. Tano; Gavin D. Portwood; Jean C. Ragusa

    Training of deep neural networks (DNNs) frequently involves optimizing several millions or even billions of parameters. Even with modern computing architectures, the computational expense of DNN training can inhibit, for instance, network architecture design optimization, hyper-parameter studies, and integration into scientific research cycles. The key factor limiting performance is that both the feed-forward

    更新日期:2020-06-26
  • Bi-Level Graph Neural Networks for Drug-Drug Interaction Prediction
    arXiv.cs.CE Pub Date : 2020-06-11
    Yunsheng Bai; Ken Gu; Yizhou Sun; Wei Wang

    We introduce Bi-GNN for modeling biological link prediction tasks such as drug-drug interaction (DDI) and protein-protein interaction (PPI). Taking drug-drug interaction as an example, existing methods using machine learning either only utilize the link structure between drugs without using the graph representation of each drug molecule, or only leverage the individual drug compound structures without

    更新日期:2020-06-26
  • Multi-scale modelling of concrete structures affected by alkali-silica reaction: Coupling the mesoscopic damage evolution and the macroscopic concrete deterioration
    arXiv.cs.CE Pub Date : 2020-06-11
    Emil R. Gallyamov; Aurelia Isabel Cuba Ramos; Mauro Corrado; Roozbeh Rezakhani; Jean-Francois Molinari

    A finite-element approach based on the first-order FE 2 homogenisation technique is formulated to analyse the alkali-silica reaction-induced damage in concrete structures, by linking the concrete degradation at the macro-scale to the reaction extent at the meso-scale. At the meso-scale level, concrete is considered as a heterogeneous material consisting of aggregates embedded in a mortar matrix. The

    更新日期:2020-06-25
  • Temperature Focusing in Microwave Cancer Hyperthermia via Pre-Corrected SAR-Based Focusing
    arXiv.cs.CE Pub Date : 2020-06-23
    Rossella Gaffoglio; Marco Righero; Giorgio Giordanengo; Marcello Zucchi; Giuseppe Vecchi

    Microwave hyperthermia aims at selectively heating cancer cells to a supra-physiological temperature. For internal tumors, this is currently achieved by means of an antenna array equipped with a proper cooling system (the water bolus) to avoid overheating of the skin. The planning of the administered heating is usually tackled by finding the antenna feedings that maximize the specific absorption rate

    更新日期:2020-06-25
  • The Power of Connection: Leveraging Network Analysis to Advance Receivable Financing
    arXiv.cs.CE Pub Date : 2020-06-24
    Ilaria Bordino; Francesco Gullo; Giacomo Legnaro

    Receivable financing is the process whereby cash is advanced to firms against receivables their customers have yet to pay: a receivable can be sold to a funder, which immediately gives the firm cash in return for a small percentage of the receivable amount as a fee. Receivable financing has been traditionally handled in a centralized way, where every request is processed by the funder individually

    更新日期:2020-06-25
  • Wavelet Augmented Regression Profiling (WARP): improved long-term estimation of travel time series with recurrent congestion
    arXiv.cs.CE Pub Date : 2020-06-23
    Alvaro Cabrejas Egea; Colm Connaughton

    Reliable estimates of typical travel times allow road users to forward plan journeys to minimise travel time, potentially increasing overall system efficiency. On busy highways, however, congestion events can cause large, short-term spikes in travel time. These spikes make direct forecasting of travel time using standard time series models difficult on the timescales of hours to days that are relevant

    更新日期:2020-06-24
  • Large scale three-dimensional manufacturing tolerant stress-constrained topology optimization
    arXiv.cs.CE Pub Date : 2020-06-23
    Gustavo Assis da Silva; Niels Aage; André Teófilo Beck; Ole Sigmund

    In topology optimization, the treatment of stress constraints for very large scale problems has so far not been tractable due to the failure of robust agglomeration methods, i.e. their inability to accurately handle the locality of the stress constraints. This paper presents a three-dimensional design methodology that alleviates this shortcoming using both deterministic and robust problem formulations

    更新日期:2020-06-24
  • Airfoil Design Parameterization and Optimization using Bézier Generative Adversarial Networks
    arXiv.cs.CE Pub Date : 2020-06-21
    wei Chen; Kevin Chiu; Mark Fuge

    Global optimization of aerodynamic shapes usually requires a large number of expensive computational fluid dynamics simulations because of the high dimensionality of the design space. One approach to combat this problem is to reduce the design space dimension by obtaining a new representation. This requires a parametric function that compactly and sufficiently describes useful variation in shapes.

    更新日期:2020-06-24
  • Surrogate sea ice model enables efficient tuning
    arXiv.cs.CE Pub Date : 2020-06-01
    Kelly Kochanski; Ivana Cvijanovic; Donald Lucas

    Predicting changes in sea ice cover is critical for shipping, ecosystem monitoring, and climate modeling. Current sea ice models, however, predict more ice than is observed in the Arctic, and less in the Antarctic. Improving the fit of these physics-based models to observations is challenging because the models are expensive to run, and therefore expensive to optimize. Here, we construct a machine

    更新日期:2020-06-24
  • Hybridisable discontinuous Galerkin solution of geometrically parametrised Stokes flows
    arXiv.cs.CE Pub Date : 2020-06-21
    Ruben Sevilla; Luca Borchini; Matteo Giacomini; Antonio Huerta

    This paper proposes a novel computational framework for the solution of geometrically parametrised flow problems governed by the Stokes equation. The proposed method uses a high-order hybridisable discontinuous Galerkin formulation and the proper generalised decomposition rationale to construct an off-line solution for a given set of geometric parameters. The generalised solution contains the information

    更新日期:2020-06-23
  • Robust and scalable h-adaptive aggregated unfitted finite elements for interface elliptic problems
    arXiv.cs.CE Pub Date : 2020-06-19
    Eric Neiva; Santiago Badia

    This work introduces a novel, fully robust and highly-scalable, $h$-adaptive aggregated unfitted finite element method for large-scale interface elliptic problems. The new method is based on a recent distributed-memory implementation of the aggregated finite element method atop a highly-scalable Cartesian forest-of-trees mesh engine. It follows the classical approach of weakly coupling nonmatching

    更新日期:2020-06-22
  • Topology synthesis of a 3-kink contact-aided compliant switch
    arXiv.cs.CE Pub Date : 2020-06-18
    B V S Nagendra Reddy; Anupam Saxena

    A topology synthesis approach to design 2D Contact-aided Compliant Mechanisms (CCMs) to trace output paths with three or more kinks is presented. Synthesis process uses three different types of external, rigid contact surfaces: circular, elliptical and rectangular: which in combination, offer intricate local curvatures that CCMs can benefit from, to deliver desired, complex output characteristics.

    更新日期:2020-06-19
  • Critical Point Calculations by Numerical Inversion of Functions
    arXiv.cs.CE Pub Date : 2020-05-30
    C. N. Parajara; G. M. Platt; F. D. Moura Neto; M. Escobar; G. B. Libotte

    In this work, we propose a new approach to the problem of critical point calculation, based on the formulation of Heidemann and Khalil (1980). This leads to a $2 \times 2$ system of nonlinear algebraic equations in temperature and molar volume, which makes possible the prediction of critical points of the mixture through an adaptation of the technique of inversion of functions from the plane to the

    更新日期:2020-06-18
  • Multiatlas Calibration of Biophysical Brain Tumor Growth Models with Mass Effect
    arXiv.cs.CE Pub Date : 2020-06-17
    Shashank Subramanian; Klaudius Scheufele; Naveen Himthani; George Biros

    We present a 3D fully-automatic method for the calibration of partial differential equation (PDE) models of glioblastoma (GBM) growth with mass effect, the deformation of brain tissue due to the tumor. We quantify the mass effect, tumor proliferation, tumor migration, and the localized tumor initial condition from a single multiparameteric Magnetic Resonance Imaging (mpMRI) patient scan. The PDE is

    更新日期:2020-06-18
  • Learning a functional control for high-frequency finance
    arXiv.cs.CE Pub Date : 2020-06-17
    Laura Leal; Mathieu Laurière; Charles-Albert Lehalle

    We use a deep neural network to generate controllers for optimal trading on high frequency data. For the first time, a neural network learns the mapping between the preferences of the trader, i.e. risk aversion parameters, and the optimal controls. An important challenge in learning this mapping is that in intraday trading, trader's actions influence price dynamics in closed loop via the market impact

    更新日期:2020-06-18
  • Morphological stability of three-dimensional cementite rods in polycrystalline system: A phase-field analysis
    arXiv.cs.CE Pub Date : 2020-06-16
    Tobias Mittnacht; Prince Gideon Kubendran Amos; Daniel Schneider; Britta Nestler

    Transformations accompanying shape-instability govern the morphological configuration and distribution of the phases in a microstructure. Owing to the influence of the microstructure on the properties of a material, the stability of three-dimensional rods in a representative polycrystalline system is extensively analysed. A multiphase-field model, which recovers the physical laws and sharp-interface

    更新日期:2020-06-16
  • Solving the Bethe-Salpeter equation on massively parallel architectures
    arXiv.cs.CE Pub Date : 2020-06-15
    Xiao ZhangUniversity of Illinois at Urbana-Champaign; Sebastian AchillesForschungszentrum JülichRWTH Aachen University; Jan WinkelmannRWTH Aachen University; Roland HaasUniversity of Illinois at Urbana-Champaign; André SchleifeUniversity of Illinois at Urbana-Champaign; Edoardo Di NapoliForschungszentrum Jülich

    The last ten years have witnessed fast spreading of massively parallel computing clusters, from leading supercomputing facilities down to the average university computing center. Many companies in the private sector have undergone a similar evolution. In this scenario, the seamless integration of software and middleware libraries is a key ingredient to ensure portability of scientific codes and guarantees

    更新日期:2020-06-15
  • An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design
    arXiv.cs.CE Pub Date : 2020-06-12
    Remy Priem; Hugo Gagnon; Ian Chittick; Stephane Dufresne; Youssef Diouane; Nathalie Bartoli

    The multi-level, multi-disciplinary and multi-fidelity optimization framework developed at Bombardier Aviation has shown great results to explore efficient and competitive aircraft configurations. This optimization framework has been developed within the Isight software, the latter offers a set of ready-to-use optimizers. Unfortunately, the computational effort required by the Isight optimizers can

    更新日期:2020-06-12
  • Algorithms and Learning for Fair Portfolio Design
    arXiv.cs.CE Pub Date : 2020-06-12
    Emily Diana; Travis Dick; Hadi Elzayn; Michael Kearns; Aaron Roth; Zachary Schutzman; Saeed Sharifi-Malvajerdi; Juba Ziani

    We consider a variation on the classical finance problem of optimal portfolio design. In our setting, a large population of consumers is drawn from some distribution over risk tolerances, and each consumer must be assigned to a portfolio of lower risk than her tolerance. The consumers may also belong to underlying groups (for instance, of demographic properties or wealth), and the goal is to design

    更新日期:2020-06-12
  • Parametric solutions of turbulent incompressible flows in OpenFOAM via the proper generalised decomposition
    arXiv.cs.CE Pub Date : 2020-06-12
    Vasileios Tsiolakis; Matteo Giacomini; Ruben Sevilla; Carsten Othmer; Antonio Huerta

    An a priori reduced order method based on the proper generalised decomposition (PGD) is proposed to compute parametric solutions involving turbulent incompressible flows of interest in an industrial context, using OpenFOAM. The PGD framework is applied for the first time to the incompressible Navier-Stokes equations in the turbulent regime, to compute a generalised solution for velocity, pressure and

    更新日期:2020-06-12
  • Improved estimations of stochastic chemical kinetics by finite state expansion
    arXiv.cs.CE Pub Date : 2020-06-12
    Tabea Waizmann; Luca Bortolussi; Andrea Vandin; Mirco Tribastone

    The stochastic kinetics of chemical reaction networks can be described by the master equation, which provides the time course evolution of the probability distribution across the discrete state space consisting of vectors of population levels of the interacting species. Since solving the master equation exactly is very difficult in general due to the combinatorial explosion of the state space size

    更新日期:2020-06-12
  • BioDynaMo: an agent-based simulation platform for scalable computational biology research
    arXiv.cs.CE Pub Date : 2020-06-11
    Lukas Breitwieser; Ahmad Hesam; Jean de Montigny; Vasileios Vavourakis; Alexandros Iosif; Jack Jennings; Marcus Kaiser; Marco Manca; Alberto Di Meglio; Zaid Al-Ars; Fons Rademakers; Onur Mutlu; Roman Bauer

    Computer simulation is an indispensable tool for studying complex biological systems. In particular, agent-based modeling is an attractive method to describe biophysical dynamics. However, two barriers limit faster progress. First, simulators do not always take full advantage of parallel and heterogeneous hardware. Second, many agent-based simulators are written with a specific research problem in

    更新日期:2020-06-11
  • Frontiers in Mortar Methods for Isogeometric Analysis
    arXiv.cs.CE Pub Date : 2020-06-11
    Christian Hesch; Ustim Khristenko; Rolf Krause; Alexander Popp; Alexander Seitz; Wolfgang Wall; Barbara Wohlmuth

    Complex geometries as common in industrial applications consist of multiple patches, if spline based parametrizations are used. The requirements for the generation of analysis-suitable models are increasing dramatically since isogeometric analysis is directly based on the spline parametrization and nowadays used for the calculation of higher-order partial differential equations. The computational,

    更新日期:2020-06-11
  • Uncovering the Underlying Physics of Degrading System Behavior Through a Deep Neural Network Framework: The Case of Remaining Useful Life Prognosis
    arXiv.cs.CE Pub Date : 2020-06-10
    Sergio Cofre-Martel; Enrique Lopez Droguett; Mohammad Modarres

    Deep learning (DL) has become an essential tool in prognosis and health management (PHM), commonly used as a regression algorithm for the prognosis of a system's behavior. One particular metric of interest is the remaining useful life (RUL) estimated using monitoring sensor data. Most of these deep learning applications treat the algorithms as black-box functions, giving little to no control of the

    更新日期:2020-06-10
  • Calibration of the von Wolffersdorff model using Genetic Algorithms
    arXiv.cs.CE Pub Date : 2020-06-10
    Francisco J. Mendez; Antonio Pasculli; Miguel A. Mendez; Nicola Sciarra

    This article proposes an optimization framework, based on Genetic Algorithms (GA), to calibrate the constitutive law of von Wolffersdorff. This constitutive law is known as Sand Hypoplasticity (SH), and allows for robust and accurate modeling of the soil behavior but requires a complex calibration involving eight parameters. The proposed optimization can automatically fit these parameters from the

    更新日期:2020-06-10
  • Physics informed deep learning for computational elastodynamics without labeled data
    arXiv.cs.CE Pub Date : 2020-06-10
    Chengping Rao; Hao Sun; Yang Liu

    Numerical methods such as finite element have been flourishing in the past decades for modeling solid mechanics problems via solving governing partial differential equations (PDEs). A salient aspect that distinguishes these numerical methods is how they approximate the physical fields of interest. Physics-informed deep learning is a novel approach recently developed for modeling PDE solutions and shows

    更新日期:2020-06-10
  • On topology optimization of large deformation contact-aided shape morphing compliant mechanisms
    arXiv.cs.CE Pub Date : 2020-06-10
    Prabhat Kumar; Roger A. Sauer; Anupam Saxena

    A topology optimization approach for designing large deformation contact-aided shape morphing compliant mechanisms is presented. Such mechanisms can be used in varying operating conditions. Design domains are described by regular hexagonal elements. Negative circular masks are employed to perform dual work, i.e., to decide material states of each element and also, to generate rigid contact surfaces

    更新日期:2020-06-10
  • Computational Design and Evaluation Methods for Empowering Non-Experts in Digital Fabrication
    arXiv.cs.CE Pub Date : 2020-06-10
    Nurcan Gecer Ulu

    Despite the increasing availability of personal fabrication hardware and services, the true potential of digital fabrication remains unrealized due to lack of computational techniques that can support 3D shape design by non-experts. This work develops computational methods that address two key aspects of content creation:(1) Function-driven design synthesis, (2) Design assessment. For design synthesis

    更新日期:2020-06-10
  • Variational Optimization for the Submodular Maximum Coverage Problem
    arXiv.cs.CE Pub Date : 2020-06-10
    Jian Du; Zhigang Hua; Shuang Yang

    We examine the \emph{submodular maximum coverage problem} (SMCP), which is related to a wide range of applications. We provide the first variational approximation for this problem based on the Nemhauser divergence, and show that it can be solved efficiently using variational optimization. The algorithm alternates between two steps: (1) an E step that estimates a variational parameter to maximize a

    更新日期:2020-06-10
  • The Computational Patient has Diabetes and a COVID
    arXiv.cs.CE Pub Date : 2020-06-09
    Pietro Barbiero; Pietro Lió

    Medicine is moving from reacting to a disease to prepare personalised and precision paths to well being. The complex and multi level pathophysiological patterns of most diseases require a systemic medicine approach and are challenging current medical therapies. Computational medicine is a vibrant interdisciplinary field that could help moving from an organ-centered to a process-oriented or systemic

    更新日期:2020-06-09
  • Deep Adversarial Koopman Model for Reaction-Diffusion systems
    arXiv.cs.CE Pub Date : 2020-06-09
    Kaushik Balakrishnan; Devesh Upadhyay

    Reaction-diffusion systems are ubiquitous in nature and in engineering applications, and are often modeled using a non-linear system of governing equations. While robust numerical methods exist to solve them, deep learning-based reduced ordermodels (ROMs) are gaining traction as they use linearized dynamical models to advance the solution in time. One such family of algorithms is based on Koopman theory

    更新日期:2020-06-09
  • Folding Simulation of Rigid Origami with Lagrange Multiplier Method
    arXiv.cs.CE Pub Date : 2020-06-09
    Yucai Hu; Haiyi Liang

    Origami crease patterns are folding paths that transform flat sheets into spatial objects. Origami patterns with a single degree of freedom (DOF) have creases that fold simultaneously. More often, several substeps are required to sequentially fold origami of multiple DOFs, and at each substep some creases fold and the rest remain fixed. In this study, we combine the loop closure constraint with Lagrange

    更新日期:2020-06-09
  • A refined dynamic finite-strain shell theory for incompressible hyperelastic materials: equations and two-dimensional shell virtual work principle
    arXiv.cs.CE Pub Date : 2020-06-09
    Xiang Yu; Yibin Fu; Hui-Hui Dai

    Based on previous work for the static problem, in this paper we first derive one form of dynamic finite-strain shell equations for incompressible hyperelastic materials that involve three shell constitutive relations. In order to single out the bending effect as well as to reduce the number of shell constitutive relations, a further refinement is performed, which leads to a refined dynamic finite-strain

    更新日期:2020-06-09
  • The aggregated unfitted finite element method on parallel tree-based adaptive meshes
    arXiv.cs.CE Pub Date : 2020-06-09
    Santiago Badia; Alberto F. Martín; Eric Neiva; Francesc Verdugo

    In this work, we present an adaptive unfitted finite element scheme that combines the aggregated finite element method with parallel adaptive mesh refinement. We introduce a novel scalable distributed-memory implementation of the resulting scheme on locally-adapted Cartesian forest-of-trees meshes. We propose a two-step algorithm to construct the finite element space at hand that carefully mixes aggregation

    更新日期:2020-06-09
  • AutoMat -- Automatic Differentiation for Generalized Standard Materials on GPUs
    arXiv.cs.CE Pub Date : 2020-06-08
    Johannes Blühdorn; Nicolas R. Gauger; Matthias Kabel

    We propose a universal method for the evaluation of generalized standard materials that greatly simplifies the material law implementation process. By means of automatic differentiation and a numerical integration scheme, AutoMat reduces the implementation effort to two potential functions. By moving AutoMat to the GPU, we close the performance gap to conventional evaluation routines and demonstrate

    更新日期:2020-06-08
  • On smooth or 0/1 designs of the fixed-mesh element-based topology optimization
    arXiv.cs.CE Pub Date : 2020-06-08
    Xiaodong Huang

    The traditional element-based topology optimization based on material penalization typically aims at a 0/1 design. Our numerical experiments reveal that the compliance of a smooth design is overestimated when material properties of boundary intermediate elements under the fixed-mesh finite element analysis are interpolated with a material penalization model. This paper proposes a floating projection

    更新日期:2020-06-08
  • Rigid-foldable generalized Miura-ori tessellations for three-dimensional curved surfaces
    arXiv.cs.CE Pub Date : 2020-06-07
    Yucai Hu; Yexin Zhou; Haiyi Liang

    Origami has shown the potential to approximate three-dimensional curved surfaces by folding through designed crease patterns on flat materials. The Miura-ori tessellation is a widely used pattern in engineering and tiles the plane when partially folded. Based on constrained optimization, this paper presents the construction of generalized Miura-ori patterns that can approximate three-dimensional parametric

    更新日期:2020-06-07
  • Generating Realistic Stock Market Order Streams
    arXiv.cs.CE Pub Date : 2020-06-07
    Junyi Li; Xitong Wang; Yaoyang Lin; Arunesh Sinha; Micheal P. Wellman

    We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions

    更新日期:2020-06-07
  • Subsurface Boundary Geometry Modeling: Applying Computational Physics, Computer Vision and Signal Processing Techniques to Geoscience
    arXiv.cs.CE Pub Date : 2020-06-06
    Raymond Leung

    This paper describes an interdisciplinary approach to geometry modeling of geospatial boundaries. The objective is to extract surfaces from irregular spatial patterns using differential geometry and obtain coherent directional predictions along the boundary of extracted surfaces to enable more targeted sampling and exploration. Specific difficulties of the data include sparsity, incompleteness, causality

    更新日期:2020-06-06
  • Geometrically nonlinear modelling of pre-stressed viscoelastic fibre-reinforced composites with application to arteries
    arXiv.cs.CE Pub Date : 2020-06-05
    I. I. Tagiltsev; A. V. Shutov

    Modelling of mechanical behaviour of pre-stressed fibre-reinforced composites is considered in a geometrically exact setting. A general approach which includes two different reference configurations is employed: one configuration corresponds to the load-free state of the structure and another one to the stress-free state of each material particle. The applicability of the approach is demonstrated in

    更新日期:2020-06-05
  • A combined XFEM phase-field computational model for crack growth without remeshing
    arXiv.cs.CE Pub Date : 2020-06-05
    Alba Muixí; Onofre Marco; Antonio Rodríguez-Ferran; Sonia Fernández-Méndez

    This paper presents an adaptive strategy for phase-field simulations with transition to fracture. The phase-field equations are solved only in small subdomains around crack tips to determine propagation, while an XFEM discretization is used in the rest of the domain to represent sharp cracks, enabling to use a coarser discretization and therefore reducing the computational cost. Crack-tip subdomains

    更新日期:2020-06-05
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