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arXiv 2021年

作者： Chakraborty, Ayan Wick, Thomas Zhuang, Xiaoying Rabczuk, Timon Division of Computational Mechanics Ton Duc Thang University Ho Chi Minh City99423 Viet Nam Faculty of Civil Engineering Ton Duc Thang University Ho Chi Minh City99423 Viet Nam Department of Geotechnical Engineering College of Civil Engineering Tongji University Shanghai China Chair of Computational Science and Simulation Technology Institute of Photonics Faculty of Mathematics and Physics Leibniz University Hannover30163 Germany Institute of Applied Mathematics Welfengarten 1 AG Wissenschaftliches Rechnen Leibniz University Hannover30167 Germany Leibniz University Hannover Germany

Deep learning has shown successful application in visual recognition and certain artificial intelligence tasks. Deep learning is also considered as a powerful tool with high flexibility to approximate functions. In the present work, functions with desired properties are devised to approximate the solutions of PDEs. Our approach is based on a posteriori error estimation in which the adjoint problem is solved for the error localization to formulate an error estimator within the framework of neural network. An efficient and easy to implement algorithm is developed to obtain a posteriori error estimate for multiple goal functionals by employing the dual-weighted residual approach, which is followed by the computation of both primal and adjoint solutions using the neural network. The present study shows that such a data-driven model based learning has superior approximation of quantities of interest even with relatively less training data. The novel algorithmic developments are substantiated with numerical test examples. The advantages of using deep neural network over the shallow neural network are demonstrated and the convergence enhancing techniques are also presented. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Machine-learning interatomic potentials enable first-principles multiscale modeling of lattice thermal conductivity in graphene/borophene heterostructures

arXiv

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arXiv 2020年

作者： Mortazavi, Bohayra Podryabinkin, Evgeny V. Roche, Stephan Rabczuk, Timon Zhuang, Xiaoying Shapeev, Alexander V. Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30157 Germany Gottfried Wilhelm Leibniz Universität Hannover Hannover Germany Skolkovo Institute of Science and Technology Skolkovo Innovation Center Nobel St. 3 Moscow143026 Russia CSIC and BIST Campus UAB Bellaterra Barcelona08193 Spain ICREA Institució Catalana de Recerca i Estudis Avancats Barcelona08010 Spain College of Civil Engineering Department of Geotechnical Engineering Tongji University Shanghai China

One of the ultimate goals of computational modeling in condensed matter is to be able to accurately compute materials properties with minimal empirical information. First-principles approaches such as the density functional theory (DFT) provide the best possible accuracy on electronic properties but they are limited to systems up to a few hundreds, or at most thousands of atoms. On the other hand, classical molecular dynamics (CMD) simulations and finite element method (FEM) are extensively employed to study larger and more realistic systems, but conversely depend on empirical information. Here, we show that machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories enable first-principles multiscale modeling, in which DFT simulations can be hierarchically bridged to efficiently simulate macroscopic structures. As a case study, we analyze the lattice thermal conductivity of coplanar graphene/borophene heterostructures, recently synthesized experimentally (Sci. Adv. 2019;5: eaax6444), for which no viable classical modeling alternative is presently available. Our MLIP-based approach can efficiently predict the lattice thermal conductivity of graphene and borophene pristine phases, the thermal conductance of complex graphene/borophene interfaces and subsequently enable the study of effective thermal transport along the heterostructures at continuum level. This work highlights that MLIPs can be effectively and conveniently employed to enable first-principles multiscale modeling via hierarchical employment of DFT/CMD/FEM simulations, thus expanding the capability for computational design of novel nanostructures. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphene

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Exploring phononic properties of two-dimensional materials using machine learning interatomic potentials

arXiv

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arXiv 2020年

作者： Mortazavi, Bohayra Novikov, Ivan S. Podryabinkin, Evgeny V. Roche, Stephan Rabczuk, Timon Shapeev, Alexander V. Zhuang, Xiaoying Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 11 Hannover30157 Germany Gottfried Wilhelm Leibniz Universität Hannover Hannover Germany Skolkovo Institute of Science and Technology Skolkovo Innovation Center Nobel St. 3 Moscow143026 Russia Institute of Materials Science University of Stuttgart Pfaffenwaldring 55 Stuttgart70569 Germany CSIC and BIST Campus UAB Bellaterra Barcelona08193 Spain ICREA Institució Catalana de Recerca i Estudis Avancats Barcelona08010 Spain College of Civil Engineering Department of Geotechnical Engineering Tongji University Shanghai China

Phononic properties are commonly studied by calculating force constants using the density functional theory (DFT) simulations. Although DFT simulations offer accurate estimations of phonon dispersion relations or thermal properties, but for low-symmetry and nanoporous structures the computational cost quickly becomes very demanding. Moreover, the computational setups may yield nonphysical imaginary frequencies in the phonon dispersion curves, impeding the assessment of phononic properties and the dynamical stability of the considered system. Here, we compute phonon dispersion relations and examine the dynamical stability of a large ensemble of novel materials and compositions. We propose a fast and convenient alternative to DFT simulations which derived from machine-learning interatomic potentials passively trained over computationally efficient ab-initio molecular dynamics trajectories. Our results for diverse two-dimensional (2D) nanomaterials confirm that the proposed computational strategy can reproduce fundamental thermal properties in close agreement with those obtained via the DFT approach. The presented method offers a stable, efficient, and convenient solution for the examination of dynamical stability and exploring the phononic properties of low-symmetry and porous 2D materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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A first-principles investigation on the structural, stability and mechanical properties of novel Ag, Au and Cu-graphdiyne magnetic semiconducting monolayers

Nano Trends

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Nano Trends 2023年 4卷

作者： Bohayra Mortazavi Fazel Shojaei Xiaoying Zhuang Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz Universität Hannover Appelstraße 1130167 Hannover Germany Department of Chemistry Faculty of Nano and Bioscience and Technology Persian Gulf University Bushehr 75169 Iran College of Civil Engineering Department of Geotechnical Engineering Tongji University 1239 Siping Road Shanghai China

Most recently, silver-metalated graphdiyne (GDY) nanomembranes have been fabricated using the wet-chemistry approach, with highly efficient response for light-driven decomposition of antibiotics ( ACS Appl. Nano Mater. 2023, 6, 7395). Inspired by this exciting advance, herein for the first time we theoretically explore the key physical properties of the single-layer M-(N)GDY (M=Cu, Ag, Au) monolayers. Spin-polarized density functional theory (DFT) results reveal that metalated GDY monolayers favor a high-spin ground state, presenting a magnetic moment of 3 µB/unitcell. The sheets are characterized as small band gap magnetic semiconductors, ranging between 0.112 and 0.960 eV, in which only one spin contributes to the band gap. Ab-initio molecular dynamics results confirm the remarkable thermal stability of the constructed graphdiyne nanomembranes at 1000 K. The dynamical stability and mechanical properties at the ground state are furthermore investigated using machine learning interatomic potentials. The ultimate tensile strengths of the metalated graphdiyne lattices are found to be decent, over 6 GPa close to the ground state, but a few folds lower than the native metal-free counterparts. The presented first-principles results confirm remarkable thermal, dynamical and mechanical stability, and appealing electronic characteristics of the metalated graphdiyne nanosheets, attractive to design advanced light-weight energy storage/conversion and spintronic nanosystems.

关键词： Graphdiyne Magnetic Machine learning First-principles Single-layer

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Correction to: Physics-informed deep learning for three-dimensional transient heat transfer analysis of functionally graded materials

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computational Mechanics 2023年第6期72卷 1315-1315页

作者： Guo, Hongwei Zhuang, Xiaoying Fu, Xiaolong Zhu, Yunzheng Rabczuk, Timon Department of Geotechnical Engineering Tongji University Shanghai P.R. China Chair of Computational Science and Simulation Technology Leibniz Universität Hannover Hannover Germany Xi’an Modern Chemistry Research Institute Xi’an China Department of Electrical and Computer Engineering UCLA Los Angeles USA Institute of Structural Mechanics Bauhaus Universität Weimar Weimar Germany

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Magnetic Excitations and Electronic Interactions in Sr2CuTeO6: A Spin-1/2 Square Lattice Heisenberg Antiferromagnet

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Physical Review Letters 2016年第23期117卷 237203-237203页

作者： P. Babkevich Vamshi M. Katukuri B. Fåk S. Rols T. Fennell D. Pajić H. Tanaka T. Pardini R. R. P. Singh A. Mitrushchenkov O. V. Yazyev H. M. Rønnow Laboratory for Quantum Magnetism Institute of Physics École Polytechnique Fédérale de Lausanne (EPFL) CH-1015 Lausanne Switzerland Chair of Computational Condensed Matter Physics Institute of Physics École Polytechnique Fédérale de Lausanne (EPFL) CH-1015 Lausanne Switzerland Institut Laue-Langevin CS 20156 F-38042 Grenoble Cedex 9 France Laboratory for Neutron Scattering and Imaging Paul Scherrer Institut CH-5232 Villigen Switzerland Department of Physics Faculty of Science University of Zagreb Bijenička cesta 32 HR-10000 Zagreb Croatia Department of Physics Tokyo Institute of Technology Meguro Tokyo 152-8551 Japan Lawrence Livermore National Laboratory Livermore California 94550 USA Department of Physics University of California Davis California 95616 USA Laboratoire Modélisation et Simulation Multi Echelle MSME UMR 8208 CNRS Université Paris-Est 5 boulevard Descartes 77454 Marne-la-Vallée France

Sr2CuTeO6 presents an opportunity for exploring low-dimensional magnetism on a square lattice of S=1/2 Cu2+ ions. We employ ab initio multireference configuration interaction calculations to unravel the Cu2+ electronic structure and to evaluate exchange interactions in Sr2CuTeO6. The latter results are validated by inelastic neutron scattering using linear spin-wave theory and series-expansion corrections for quantum effects to extract true coupling parameters. Using this methodology, which is quite general, we demonstrate that Sr2CuTeO6 is an almost ideal realization of a nearest-neighbor Heisenberg antiferromagnet but with relatively weak coupling of 7.18(5) meV.

关键词： Antiferromagnetism Exchange interaction Spin waves Strongly correlated systems Inelastic neutron scattering Quantum chemistry methods

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The impact of AI on engineering design procedures for dynamical systems

arXiv

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arXiv 2024年

作者： de Payrebrune, Kristin M. Flaßkamp, Kathrin Ströhla, Tom Sattel, Thomas Bestle, Dieter Röder, Benedict Eberhard, Peter Peitz, Sebastian Stoffel, Marcus Rutwik, Gulakala Aditya, Borse Wohlleben, Meike Sextro, Walter Raff, Maximilian Remy, C. David Yadav, Manish Stender, Merten van Delden, Jan Lüddecke, Timo Langer, Sabine C. Schultz, Julius Blech, Christopher RPTU Kaiserslautern-Landau Institute for Computational Physics in Engneering Gottlieb-Daimler-Straße 74 Kaiserslautern67663 Germany Saarland University Systems Engineering Systems Modeling and Simulation Campus Saarbrücken66123 Germany TU Ilmenau Mechatronics group Max-Planck-Ring 12 Ilmenau98693 Germany Brandenburg University of Technology Cottbus-Senftenberg Chair of Engineering Mechanics and Vehicle Dynamics Siemens-Halske-Ring 14 Cottbus03046 Germany University of Stuttgart Institute of Engineering and Computational Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany TU Dortmund Department of Computer Science & Lamarr Institute Joseph-von-Fraunhofer-Str. 25 Dortmund44227 Germany RWTH Aachen University Institute of General Mechanics Eilfschornsteinstraße 18 Aachen52062 Germany Paderborn University Faculty of Mechanical Engineering Dynamics and Mechatronics Warburger Str. 100 Paderborn33098 Germany University of Stuttgart Institute for Nonlinear Mechanics Pfaffenwaldring 9 Stuttgart70569 Germany Technische Universität Berlin Chair of Cyber-Physical Systems in Mechanical Engineering Straße des 17. Juni 135 Berlin10623 Germany University of Göttingen Institute of Informatics Goldschmidtstr. 7 Göttingen37077 Germany Technische Universität Braunschweig Institute for Acoustics and Dynamics Langer Kamp 19 Braunschweig38106 Germany

Artificial intelligence (AI) is driving transformative changes across numerous fields, revolutionizing conventional processes and creating new opportunities for innovation. The development of mechatronic systems is undergoing a similar transformation. Over the past decade, modeling, simulation, and optimization techniques have become integral to the design process, paving the way for the adoption of AI-based methods. In this paper, we examine the potential for integrating AI into the engineering design process, using the V-model from the VDI guideline 2206, considered the state-of-the-art in product design, as a foundation. We identify and classify AI methods based on their suitability for specific stages within the engineering product design workflow. Furthermore, we present a series of application examples where AI-assisted design has been successfully implemented by the authors. These examples, drawn from research projects within the DFG Priority Program SPP 2353: Daring More Intelligence - Design Assistants in Mechanics and Dynamics, showcase a diverse range of applications across mechanics and mechatronics, including areas such as acoustics and robotics. © 2024, CC BY.

关键词： Mechatronics

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Phase field modeling of hydraulic fracture propagation in spatially variable rock masses

引用

IOP Conference Series: Earth and Environmental science 2020年第2期570卷

作者： F Y Chen S W Zhou X Y Zhuang W G Zhang Chair of Computational Science and Simulation Technology Department of Mathematics and Physics Leibniz University Hannover Hannover 30167 Germany School of Civil Engineering Chongqing University Chongqing 400045 China Department of Geotechnical Engineering College of Civil Engineering Tongji University Shanghai 200092 P.R. China Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing University Chongqing 400045 China National Joint Engineering Research Center of Geohazards Prevention in the Reservoir Areas Chongqing University Chongqing 400045 China

Due to differences in mineral compositions, sedimentary conditions, stress history, and geological processes, the spatial variability of the mechanical properties of rock masses is usually considerable. Therefore, the influence of the spatial variability of rock mass parameters on the irregular propagation of hydraulic fractures should be studied. In this work, an efficient approach is proposed for studying the irregular propagation of hydraulic fractures considering the spatial variability of rock mass parameters; this approach is based on the phase field method and random field theory. Combined with random field theory, the phase field method is adopted to simulate the fracture propagation in a spatially variable rock mass. Random fields of the Young's modulus are generated using the Cholesky decomposition method and then embedded into the phase field model. The influences of different scales of fluctuation of the rock mass parameters' random fields on the fracture shape under fluid-driven conditions are investigated in this study. The results indicate that the spatial variability of the Young's modulus has a significant influence on the propagation of hydraulic fractures.

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The Influence of Discontinuity-Induced Fringing Effect on the Output Performance of Contact-Separation Mode Triboelectric Nanogenerators: Experiment and Modeling Studies

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Advanced Synthesis & Catalysis 2024年第18期366卷

作者： Teresa Cheng Han Hu Navid Valizadeh Liu Qiong Florian Bittner Ling Yang Timon Rabczuk Xiaoning Jiang Xiaoying Zhuang Chair of Computational Science and Simulation Technology Faculty of Mathematics and Physics Leibniz University Hannover 30167 Hannover Germany Laboratory of Nano and Quantum Engineering (LNQE) Leibniz University Hannover 30167 Hannover Germany Institute of Plastics and Circular Economy (IKK) Leibniz University Hannover 30167 Garbsen Germany Geriatric Department Shanghai Fourth People's Hospital Affiliated to Tongji University Shanghai 30167 China Institute of Structural Mechanics Bauhaus University 99423 Weimar Germany Department of Mechanical and Aerospace Engineering North Carolina State University Raleigh NC 27695 USA College of Civil Engineering Department of Geotechnical Engineering Tongji University Shanghai 20092 China

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26th Annual computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

引用

BMC NEUROscience 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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