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arXiv 2021年

作者： Golshan, Shahab Munch, Peter Gassmöller, Rene Kronbichler, Martin Blaisa, Bruno Department of Chemical Engineering École Polytechique de Montréal PO Box 6079 Stn Centre-Ville MontréalQCH3C 3A7 Canada Continuum Simulations Institute of Material Systems Modeling Helmholtz-Zentrum Hereon GmbH Germany Max-Planck-Str. 1 Geesthacht21502 Germany Institute for Computational Mechanics Technical University of Munich Boltzmannstr. 15 Garching85748 Germany Department of Geological Sciences University of Florida GainesvilleFL United States Division of Scientific Computing Department of Information Technology Uppsala University Box 337 Uppsala75105 Sweden

Approximately 75% of the raw material and 50% of the products in the chemical industry are granular materials. The Discrete Element Method (DEM) provides detailed insights of phenomena at particle scale and it is therefore often used for modeling granular materials. However, because DEM tracks the motion and contact of individual particles separately, its computational cost increases non-linearly O(np log(np)) - O(n2p) depending on the algorithm) with the number of particles (np). In this article, we introduce a new open-source parallel DEM software with load balancing: Lethe-DEM. Lethe-DEM, a module of Lethe, consists of solvers for two-dimensional and three-dimensional DEM simulations. Load-balancing allows Lethe-DEM to significantly increase the parallel efficiency by ≈ 25 − 70% depending on the granular simulation. We explain the fundamental modules of Lethe-DEM, its software architecture, and the governing equations. Furthermore, we verify Lethe-DEM with several tests including analytical solutions and comparison with other software. Comparisons with experiments in a flat-bottomed silo, wedge-shaped silo, and rotating drum validate Lethe-DEM. We investigate the strong and weak scaling of Lethe-DEM with 1 ≤ nc ≤ 192 and 32 ≤ nc ≤ 320 processes, respectively, with and without load-balancing. The strong-scaling analysis is performed on the wedge-shaped silo and rotating drum simulations, while for the weak-scaling analysis, we use a dam break simulation. The best scalability of Lethe-DEM is obtained in the range of 5000 ≤ np/nc ≤ 15 000. Finally, we demonstrate that large scale simulations can be carried out with Lethe-DEM using the simulation of a three-dimensional cylindrical silo with np = 4.3 × 106 on 320 cores. Copyright © 2021, The Authors. All rights reserved.

关键词： Finite difference method

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High-order partitioned spectral deferred correction solvers for multiphysics problems

arXiv

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arXiv 2019年

作者： Huang, Daniel Z. Pazner, Will Persson, Per-Olof Zahr, Matthew J. Institute for Computational and Mathematical Engineering Stanford University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States Department of Mathematics University of California Berkeley United States Department of Aerospace and Mechanical Engineering University of Notre Dame United States

We present an arbitrarily high-order, conditionally stable, partitioned spectral deferred correction (SDC) method for solving multiphysics problems using a sequence of pre-existing single-physics solvers. This method extends the work in [1, 2], which used implicit-explicit Runge-Kutta methods (IMEX) to build high-order, partitioned multiphysics solvers. We consider a generic multiphysics problem modeled as a system of coupled ordinary differential equations (ODEs), coupled through coupling terms that can depend on the state of each subsystem;therefore the method applies to both a semi-discretized system of partial differential equations (PDEs) or problems naturally modeled as coupled systems of ODEs. The sufficient conditions to build arbitrarily high-order partitioned SDC schemes are derived. Based on these conditions, various of partitioned SDC schemes are designed. The stability of the first-order partitioned SDC scheme is analyzed in detail on a coupled, linear model problem. We show that the scheme is conditionally stable, and under conditions on the coupling strength, the scheme can be unconditionally stable. We demonstrate the performance of the proposed partitioned solvers on several classes of multiphysics problems including a simple linear system of ODEs, advection-diffusion-reaction systems, and fluid-structure interaction problems with both incompressible and compressible flows, where we verify the design order of the SDC schemes and study various stability properties. We also directly compare the accuracy, stability, and cost of the proposed partitioned SDC solver with the partitioned IMEX method in [1, 2] on this suite of test problems. The results suggest that the high-order partitioned SDC solvers are more robust than the partitioned IMEX solvers for the numerical examples considered in this work, while the IMEX methods require fewer implicit solves. Copyright © 2019, The Authors. All rights reserved.

关键词： Stability

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Deep nitsche method: Deep ritz method with essential boundary conditions

arXiv

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arXiv 2019年

作者： Liao, Yulei Ming, Pingbing LSEC Institute of Computational Mathematics and Scientific/Engineering Computing AMSS Chinese Academy of Sciences No. 55 East Road Zhong-Guan-Cun Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China

We propose a new method to deal with the essential boundary conditions encountered in the deep learning-based numerical solvers for partial differential equations. The trial functions representing by deep neural networks are non-interpolatory, which makes the enforcement of the essential boundary conditions a nontrivial matter. Our method resorts to Nitsche's variational formulation to deal with this difficulty, which is consistent, and does not require significant extra computational costs. We prove the error estimate in the energy norm and illustrate the method on several representative problems posed in at most 100 dimension. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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L-Sweeps: A scalable, parallel preconditioner for the high-frequency Helmholtz equation

arXiv

引用

arXiv 2019年

作者： Taus, Matthias Zepeda-Núñez, Leonardo Hewett, Russell J. Demanet, Laurent Institute of Analysis and Scientific Computing Vienna University of Technology Department of Mathematics Massachusetts Institute of Technology Computational Research Division Lawrence Berkeley National Laboratory Department of Mathematics University of California Berkeley Department of Mathematics University of Wisconsin-Madison Total E&P Research and Technology United States Department of Mathematics Virginia Tech

We present the first fast solver for the high-frequency Helmholtz equation that scales optimally in parallel, for a single right-hand side. The L-sweeps approach achieves this scalability by departing from the usual propagation pattern, in which information flows in a 180 degree cone from interfaces in a layered decomposition. Instead, with L-sweeps, information propagates in 90_ cones induced by a checkerboard domain decomposition (CDD). We extend the notion of accurate transmission conditions to CDDs and introduce a new sweeping strategy to efficiently track the wave fronts as they propagate through the CDD. The new approach decouples the subdomains at each wave front, so that they can be processed in parallel, resulting in better parallel scalability than previously demonstrated in the literature. The method has an overall O ((N/p) logω) empirical run-time for N = ndtotal degrees-of-freedom in a d-dimensional problem, frequency ω, and p = O(n) processors. We introduce the algorithm and provide a complexity analysis for our parallel implementation of the solver. We corroborate all claims in several two- and three-dimensional numerical examples involving constant, smooth, and discontinuous wave speeds. Copyright © 2019, The Authors. All rights reserved.

关键词： Helmholtz equation

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Drug3D-DTI: Improved Drug-target Interaction Prediction by Incorporating Spatial Information of Small Molecules

Drug3D-DTI: Improved Drug-target Interaction Prediction by I...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhirui Liao Xiaodi Huang Hiroshi Mamitsuka Shanfeng Zhu School of Computer Science Fudan University Shanghai China Institute of Artificial Intelligence Biomedicine Nanjing University Nanjing China School of Computing Mathematics and Engineering Charles Sturt University Albury NSW Australia Bioinformatics Center Institute for Chemical Research Kyoto University Uji Kyoto Japan Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665429825

A number of machine learning (ML) approaches for drug discovery have been available that rely only on sequential (1D) and planar (2D) information without effectively using the 3D information for generating features of drugs. However, 3D information of small molecules can reflect relative position of atoms more directly, which affects molecular properties. In this work, we present a new deep learning model called Drug3D-DTI for drug-target interaction prediction. Drug3D-DTI takes advantage of molecular spatial information, i.e., atom proximity in three-dimensional (3D) structures. We comprehensively evaluated the performance of Drug3D-DTI on two datasets with two tasks of regression and classification. In particular, we compared Drug3D-DTI with several existing methods including the two cutting-edge methods for compound-protein interaction prediction. From the experimental results, Drug3D-DTI clearly outperformed other methods under all settings. Further, this performance improvement was validated by ablation experiments and a case study. The implementation of Drug3D-DTI is available at (https://***/zhiruiliao/Drug3D-DTI).

关键词： Drugs Proteins Deep learning Solid modeling Three-dimensional displays Conferences Predictive models

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On the spectrum of mesons in quenched Sp(2N) gauge theories

arXiv

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arXiv 2023年

作者： Bennett, Ed Holligan, Jack Hong, Deog Ki Lee, Jong-Wan Lin, C.-J. David Lucini, Biagio Piai, Maurizio Vadacchino, Davide Swansea Academy of Advanced Computing Swansea University Fabian Way Swansea WalesSA1 8EN United Kingdom Biomedical and Physical Sciences Building Michigan State University East LansingMI48824 United States Department of Physics Pusan National University Busan46241 Korea Republic of Institute for Extreme Physics Pusan National University Busan46241 Korea Republic of Daejeon34126 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Center for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Department of Mathematics Faculty of Science and Engineering Swansea University Fabian Way Swansea WalesSA1 8EN United Kingdom Department of Physics Faculty of Science and Engineering Swansea University Singleton Park Swansea WalesSA2 8PP United Kingdom Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

We report the findings of our extensive study of the spectra of flavoured mesons in lattice gauge theories with symplectic gauge group and fermion matter content treated in the quenched approximation. For the Sp(4), Sp(6), and Sp(8) gauge groups, the (Dirac) fermions transform in either the fundamental, or the 2-index, antisymmetric or symmetric, representations. This study sets the stage for future precision calculations with dynamical fermions in the low mass region of lattice parameter space. Our results have potential phenomenological applications ranging from composite Higgs models, to top (partial) compositeness, to dark matter models with composite, strong-coupling dynamical origin. Having adopted the Wilson flow as a scale-setting procedure, we apply Wilson chiral perturbation theory to extract the continuum and massless limits for the observables of interest. The resulting measurements are used to perform a simplified extrapolation to the large-N limit, hence drawing a preliminary connection with gauge theories with unitary groups. We conclude with a brief discussion of the Weinberg sum rules. © 2023, CC BY.

关键词： Perturbation techniques

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Towards provably efficient quantum algorithms for large-scale machine-learning models

arXiv

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arXiv 2023年

作者： Liu, Junyu Liu, Minzhao Liu, Jin-Peng Ye, Ziyu Wang, Yunfei Alexeev, Yuri Eisert, Jens Jiang, Liang Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Chicago Quantum Exchange ChicagoIL60637 United States Kadanoff Center for Theoretical Physics The University of Chicago ChicagoIL60637 United States qBraid Co. ChicagoIL60615 United States SeQure ChicagoIL60615 United States Department of Physics The University of Chicago ChicagoIL60637 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States Simons Institute for the Theory of Computing University of California BerkeleyCA94720 United States Department of Mathematics University of California BerkeleyCA94720 United States Center for Theoretical Physics Massachusetts Institute of Technology CambridgeMA02139 United States Martin A. Fisher School of Physics Brandeis University WalthamMA02453 United States Dahlem Center for Complex Quantum Systems Free University Berlin Berlin14195 Germany

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T2 × polylog(n)), where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that faulttolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Differential equations

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Recovery of meteorites using an autonomous drone and machine learning

arXiv

引用

arXiv 2021年

作者： Citron, Robert I. Jenniskens, Peter Watkins, Christopher Sinha, Sravanthi Shah, Amar Raissi, Chedy Devillepoix, Hadrien Albers, Jim Department of Earth and Planetary Sciences University of California Davis95616 United States SETI Institute Carl Sagan Center Mountain ViewCA94043 United States NASA Ames Research Center Moffett FieldCA94035 United States Commonwealth Scientific and Industrial Research Organisation Scientific Computing ClaytonVIC3800 Australia Holberton School of Software Engineering San FranciscoCA94111 United States Cambridge University Dep. of Engineering Computational and Biological Learning CambridgeCB2 1PZ United Kingdom Institut National de Recherche en Informatique et en Automatique France Space Science Technology Centre School of Earth and Planetary Sciences Curtin University GPO Box U1987 PerthWA6845 Australia

The recovery of freshly fallen meteorites from tracked and triangulated meteors is critical to determining their source asteroid families. However, locating meteorite fragments in strewn fields remains a challenge with very few meteorites being recovered from the meteors triangulated in past and ongoing meteor camera networks. We examined if locating meteorites can be automated using machine learning and an autonomous drone. Drones can be programmed to fly a grid search pattern and take systematic pictures of the ground over a large survey area. Those images can be analyzed using a machine learning classifier to identify meteorites in the field among many other features. Here, we describe a proof-of-concept meteorite classifier that deploys off-line a combination of different convolution neural networks to recognize meteorites from images taken by drones in the field. The system was implemented in a conceptual drone setup and tested in the suspected strewn field of a recent meteorite fall near Walker Lake, Nevada. © 2021, CC BY.

关键词： Meteorites

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Double parameters full-waveform inversion for velocity and density in the time domain

Double parameters full-waveform inversion for velocity and d...

引用

CPS/SEG北京2018国际地球物理会议暨展览

作者： Wensheng Zhang UCAS LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences

In this paper, the double parameters full-waveform inversion for velocity and density simultaneously in the time domain is investigated. Comparing with the single parameter inversion, the double parameters inversion is more difficult because of the interactive influences between velocity and density. The forward is solved by the efficient staggered-grid difference scheme with the perfectly matched absorbing layer. The inversion is an optimization iterative process based on LBFGS algorithm. The combination of L2 regularization and TV regularization and the stepwise inversion strategy from low frequency to high frequency are applied to overcome the ill-posedness. Moreover, the adaptive regularization parameter is adopted to improve inversion precision. The double parameters inversion for Marmousi model is completed. The numerical results show the double parameters inversion can yield good inversion results of velocity and density even in the case of noisy data. The numerical computations show the correctness and effectiveness of the method in this paper.

关键词： FWI Double parameters full-waveform inversion for velocity and density in the time domain

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Sp(2N) Lattice Gauge Theories and Extensions of the Standard Model of Particle Physics

arXiv

引用

arXiv 2023年

作者： Bennett, Ed Holligan, Jack Hong, Deog Ki Hsiao, Ho Lee, Jong-Wan Lin, C.-J. David Lucini, Biagio Mesiti, Michele Piai, Maurizio Vadacchino, Davide Fabian Way SwanseaSA1 8EN United Kingdom Michigan State University Biomedical and Physical Sciences Building East LansingMI48824 United States Physical Sciences Complex University of Maryland College ParkMD20742 United States Department of Physics Pusan National University Busan46241 Korea Republic of Institute of Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Institute for Extreme Physics Pusan National University Busan46241 Korea Republic of Daejeon34126 Korea Republic of Center for High Energy Physics Chung-Yuan Christian University Chung-Li32023 Taiwan Centre for Theoretical and Computational Physics National Yang Ming Chiao Tung University 1001 Ta-Hsueh Road Hsinchu30010 Taiwan Fabian Way SwanseaSA1 8EN United Kingdom Steinbuch Centre for Computing Karlsruher Institut für Technologie Zirkel 2 Karlsruhe76131 Germany Department of Physics Faculty of Science and Engineering Swansea University Singleton Park Campus Singleton Park SwanseaSA2 8PP United Kingdom School of Mathematics Hamilton Mathematics Institute Trinity College Dublin 2 Ireland Centre for Mathematical Sciences University of Plymouth PlymouthPL4 8AA United Kingdom

We review the current status of the long-term programme of numerical investigation of Sp(2N) gauge theories with and without fermionic matter content. We start by introducing the phenomenological as well as theoretical motivations for this research programme, which are related to composite Higgs models, models of partial top compositeness, dark matter models, and in general to the physics of strongly coupled theories and their approach to the large-N limit. We summarise the results of lattice studies conducted so far in the Sp(2N) Yang-Mills theories, measuring the string tension, the mass spectrum of glueballs and the topological susceptibility, and discuss their large-N extrapolation. We then focus our discussion on Sp(4), and summarise numerical measurements of mass and decay constant of mesons in the theories with fermion matter in either the fundamental or the antisymmetric representation, first in the quenched approximation, and then with dynamical fermions. We finally discuss the case of dynamical fermions in mixed representations, and exotic composite fermion states such as the chimera baryons. We conclude by sketching the future stages of the programme. And we describe our approach to open access. © 2023, CC BY.

关键词： Mass spectrometry

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