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An Efficient Numerical Method for the Anisotropic Phase Field Dendritic Crystal Growth Model

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SSRN

SSRN 2023年

作者： Guo, Yayu Azaïez, Mejdi Xu, Chuanju School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High Performance Scientific Computing Xiamen University Xiamen361005 China Université de Bordeaux I2M UMR CNRS 5295 Talence33400 France

In this paper, we propose and analyze an efficient numerical methodfor the anisotropic phase field dendritic crystalgrowth model, which is challenging because we are facingthe nonlinear coupling and anisotropic coefficient in the *** proposed method is a two-step scheme. In the first step, an intermediate solution is computed byusing BDF schemes of order up to three for both the phase-field and heat *** the second step the intermediate solution is stabilized by multiplyingan auxiliary variable. The key of the second step is to stabilize the overall schemewhile maintaining the convergence order of the stabilized *** order to overcome the difficulty caused by thegradient-dependent anisotropic coefficient and the nonlinear terms,some stabilization terms are added to the BDF schemes in the first *** second step makes use of a generalized auxiliary variable approachwith relaxation. The Fourier spectral method is applied for the spatial *** analysis shows that the proposed scheme is unconditionally stable andhas accuracy in time up to third *** also provide a sophisticated implementationshowing that the computational complexity of our schemes is equivalent tosolving two linear equations and some algebraic *** the best of our knowledge, this is the cheapest unconditionally stable schemesreported in the literature. Some numerical examples are given to verify the efficiency of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Anisotropy

Progressive Unsupervised Domain Adaptation for Image-based Person Re-Identification

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Progressive Unsupervised Domain Adaptation for Image-based P...

International Conference on Pattern Recognition

作者： Mingliang Yang Jing Zhao Da Huang Ji Wang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781728188089;9781728188096

Unsupervised domain adaptation (UDA) has emerged as an effective paradigm for reducing the huge manual annotation cost for Person Re-Identification (Re-ID). Many of the recent UDA methods for Re-ID are clustering-based and select all the pseudo-label samples in each iteration for the model training. However, there are many wrong labeled samples that will mislead the model optimization under this circumstance. To solve this problem, we propose a Progressive Unsupervised Domain Adaptation (PUDA) framework for image-based Person Re-ID to reduce the negative effect of wrong pseudo-label samples on the model training process. Specifically, we first pretrain a CNN model on a labeled source dataset, then finetune the model on unlabeled target dataset with the following three steps iteratively: 1) estimating pseudo-labels for all the images in the target dataset with the model trained in the last iteration; 2) extending the training set by adding pseudo-label samples with higher label confidence; 3) updating the CNN model with the expanded training set in a supervised manner. During the iteration process, the number of pseudo-label samples added increased progressively. In particular, a Moderate Initial Selections (MIS) strategy for pseudo-label sampling is also proposed to reduce the negative impacts of random noise features in the early iterations and mislabeled samples in the late iterations on the model. The proposed framework with MIS strategy is validated on the Duke-to-Market, Market-to-Duke unsupervised domain adaptation tasks and achieves improvements of 4.2 points (absolute, i.e., 80.0% vs. 75.8%) and 1.7 points (absolute, i.e., 70.7% vs. 69.0%) in mAP correspondingly.

关键词： Training Adaptation models Annotations Manuals Feature extraction Pattern recognition Reliability

Dynamic recrystallization-induced temperature insensitivity of yield stress in single-crystal Al1.2CrFeCoNi micropillars

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Science China(Technological Sciences) 2021年第1期64卷 11-22页

作者： HUANG RuiRui ZHANG Qian ZHANG Xuan LI JianGuo CAO TangQing YAO JiaHao XUE YunFei GAO HuaJian LI XiaoYan Center for Advanced Mechanics and Materials Applied Mechanics LaboratoryDepartment of Engineering MechanicsTsinghua UniversityBeijing 100084China School of Materials Science and Engineering Beijing Institute of TechnologyBeijing 100081China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Mechanical and Aerospace Engineering College of EngineeringNanyang Technological UniversitySingapore 639798Singapore Institute of High Performance Computing A*STARSingapore 138632Singapore

high-entropy alloys, a new class of metallic materials, exhibit excellent mechanical properties at high temperatures. In spite of the worldwide interest, the underlying mechanisms for temperature dependence of mechanical properties of these alloys remain poorly understood. Here, we systemically investigate the mechanical behaviors and properties of Al_(1.2)CrFeCoNi(comprising a body-centered cubic phase) and Al_(0.3)CrFeCoNi(comprising a face-centered cubic phase) single-crystal micropillars with three orientations([100], [110], and [111]) at temperatures varying from 300 to 675 K by using in situ compression of micropillars inside a scanning electron microscope. The results show that the yield stresses of Al_(1.2)CrFeCoNi micropillars are insensitive to temperature changes, and their flow stresses and work hardening rates increase slightly with increasing temperature from 300 to550 K, which differs from the typical temperature dependence of yield/flow stresses in metals and alloys. In contrast,Al_(0.3)CrFeCoNi micropillars exhibit typical thermal softening. Furthermore, it is found that the Al_(1.2)CrFeCoNi micropillars exhibit a transition from homogenous deformation to localized deformation at a critical temperature, while the Al_(0.3)CrFeCoNi micropillars always maintain a well-distributed and fine slip deformation. Detailed transmission electron microscopy analyses reveal that dynamic recrystallization(involving dislocation tangles, and formation of dislocation cell structures and sub-grains)plays a key role in the observed temperature insensitivity of the yield stress and increasing flow stress(and work hardening rate)with increasing temperature in the Al_(1.2)CrFeCoNi micropillars, and that thermally activated dislocation slip leads to thermal softening of the Al_(0.3)CrFeCoNi micropillars. The differences in deformation modes and temperature dependence of the mechanical properties between Al_(1.2)CrFeCoNi and Al_(0.3)CrFeCoNi essentially originate from the d

关键词： high entropy alloy temperature insensitivity dynamic recrystallization micropillar deformation twin

Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions

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arXiv 2023年

作者： Lin, Bin Mao, Zhiping Wang, Zhicheng Karniadakis, George Em School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Xiamen361005 China Laboratory of Ocean Energy Utilization Ministry of Education School of Energy and Power Engineering Dalian University of Technology Dalian China Division of Applied Mathematics Brown University ProvidenceRI02906 United States

Physics-informed Neural Networks (PINNs) have been shown as a promising approach for solving both forward and inverse problems of partial differential equations (PDEs). Meanwhile, the neural operator approach, including methods such as Deep Operator Network (DeepONet) and Fourier neural operator (FNO), has been introduced and extensively employed in approximating solution of PDEs. Nevertheless, to solve problems consisting of sharp solutions poses a significant challenge when employing these two approaches. To address this issue, we propose in this work a novel framework termed Operator Learning Enhanced Physics-informed Neural Networks (OL-PINN). Initially, we utilize DeepONet to learn the solution operator for a set of smooth problems relevant to the PDEs characterized by sharp solutions. Subsequently, we integrate the pre-trained DeepONet with PINN to resolve the target sharp solution problem. We showcase the efficacy of OL-PINN by successfully addressing various problems, such as the nonlinear diffusion-reaction equation, the Burgers equation and the incompressible Navier-Stokes equation at high Reynolds number. Compared with the vanilla PINN, the proposed method requires only a small number of residual points to achieve a strong generalization capability. Moreover, it substantially enhances accuracy, while also ensuring a robust training process. Furthermore, OL-PINN inherits the advantage of PINN for solving inverse problems. To this end, we apply the OL-PINN approach for solving problems with only partial boundary conditions, which usually cannot be solved by the classical numerical methods, showing its capacity in solving ill-posed problems and consequently more complex inverse problems. Copyright © 2023, The Authors. All rights reserved.

关键词： Partial differential equations

Provably convergent Newton-Raphson methods for recovering primitive variables with applications to physical-constraint-preserving Hermite WENO schemes for relativistic hydrodynamics

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arXiv 2023年

作者： Cai, Chaoyi Qiu, Jianxian Wu, Kailiang School of Mathematical Sciences Xiamen University Fujian Xiamen361005 China School of Mathematical Sciences Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific Computing Xiamen University Fujian Xiamen361005 China Shenzhen518055 China

The relativistic hydrodynamics (RHD) equations have three crucial intrinsic physical constraints on the primitive variables: positivity of pressure and density, and subluminal fluid velocity. However, numerical simulations can violate these constraints, leading to nonphysical results or even simulation failure. Designing genuinely physical-constraint-preserving (PCP) schemes is very difficult, as the primitive variables cannot be explicitly reformulated using conservative variables due to relativistic effects. In this paper, we propose three efficient Newton–Raphson (NR) methods for robustly recovering primitive variables from conservative variables. Importantly, we rigorously prove that these NR methods are always convergent and PCP, meaning they preserve the physical constraints throughout the NR iterations. The discovery of these robust NR methods and their PCP convergence analyses are highly nontrivial and technical. As an application, we apply the proposed NR methods to design PCP finite volume Hermite weighted essentially non-oscillatory (HWENO) schemes for solving the RHD equations. Our PCP HWENO schemes incorporate high-order HWENO reconstruction, a PCP limiter, and strong-stability-preserving time discretization. We rigorously prove the PCP property of the fully discrete schemes using convex decomposition techniques. Moreover, we suggest the characteristic decomposition with rescaled eigenvectors and scale-invariant nonlinear weights to enhance the performance of the HWENO schemes in simulating large-scale RHD problems. Several demanding numerical tests are conducted to demonstrate the robustness, accuracy, and high resolution of the proposed PCP HWENO schemes and to validate the efficiency of our NR methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Newton-Raphson method

Automated verification of countermeasure against detector-control attack in quantum key distribution

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arXiv 2023年

作者： Acheva, Polina Zaitsev, Konstantin Zavodilenko, Vladimir Losev, Anton Huang, Anqi Makarov, Vadim Russian Quantum Center Skolkovo Moscow121205 Russia NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia HSE University Moscow101000 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Attacks that control single-photon detectors in quantum key distribution using tailored bright illumination are capable of eavesdropping the secret key. Here we report an automated testbench that checks the detector’s vulnerabilities against these attacks. We illustrate its performance by testing a free-running detector that includes a rudimentary countermeasure measuring an average photocurrent. While our testbench automatically finds the detector to be controllable in a continuous-blinding regime, the countermeasure registers photocurrent significantly exceeding that in a quantum regime, thus revealing the attack. We then perform manually a pulsed blinding attack, which controls the detector intermittently. This attack is missed by the countermeasure in a wide range of blinding pulse durations and powers, still allowing to eavesdrop the key. We make recommendations for improvement of both the testbench and countermeasure. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

Variational quantum process tomography of unitaries

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Physical Review A 2022年第3期105卷 032427-032427页

作者： Shichuan Xue Yong Liu Yang Wang Pingyu Zhu Chu Guo Junjie Wu Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China College of Information and Communication National University of Defense Technology Xi'an 710006 China Henan Key Laboratory of Quantum Information and Cryptography Zhengzhou Henan 450000 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha 410081 China

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to eight qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least two orders of magnitude less than required by the standard quantum process tomography.

关键词： Machine learning Quantum information processing Quantum simulation Quantum tomography

DL-Chain: Scalable and Stable Blockchain Sharding with high Concurrency via Dual-Layer Consensus

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arXiv 2024年

作者： Lin, You Li, Mingzhe Wei, Qingsong Liu, Yong Goh, Siow Mong Rick Zhang, Jin The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong The Institute of High Performance Computing A*STAR Singapore Singapore The Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Sharding enhances blockchain scalability by partitioning nodes into multiple groups for concurrent transaction processing. Configuring a large number of small shards helps improve the transaction concurrency of a sharding system. However, it increases the fraction of malicious nodes within each shard, easily leading to shard corruption and jeopardizing system security. Some existing works have attempted to improve concurrency by reducing the shard size while maintaining security. However, they often require frequent and time-consuming recovery of corrupted shards, leading to severe system stagnation. Also, they usually require network-wide consensus to guarantee security, which limits scalability. To address these issues, we propose DL-Chain, a blockchain sharding system that can securely provide high concurrency with stable and scalable performance. Our core idea is a Dual-Layer architecture and consensus, which consists of numerous smaller proposer shards (PSs) for transaction processing and multiple larger finalizer committees (FCs) for transaction finalization. To avoid system stagnation and thus guarantee stable performance, we ensure PSs’ liveness even if they are corrupted through the cooperation of PSs and FCs, thus eliminating the recovery process of corrupted PSs. To better trade-off security and scalability, we fine-tune the FCs to enable multiple FCs to coexist securely. As a result, DL-Chain allows a larger fraction of malicious nodes in each PS ( Copyright © 2024, The Authors. All rights reserved.

关键词： Blockchain

AN EFFICIENT NUMERICAL METHOD FOR THE ANISOTROPIC PHASE FIELD DENDRITIC CRYSTAL GROWTH MODEL

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arXiv 2023年

In this paper, we propose and analyze an efficient numerical method for the anisotropic phase field dendritic crystal growth model, which is challenging because we are facing the nonlinear coupling and anisotropic coefficient in the model. The proposed method is a two-step scheme. In the first step, an intermediate solution is computed by using BDF schemes of order up to three for both the phase-field and heat equations. In the second step the intermediate solution is stabilized by multiplying an auxiliary variable. The key of the second step is to stabilize the overall scheme while maintaining the convergence order of the stabilized solution. In order to overcome the difficulty caused by the gradient-dependent anisotropic coefficient and the nonlinear terms, some stabilization terms are added to the BDF schemes in the first step. The second step makes use of a generalized auxiliary variable approach with relaxation. The Fourier spectral method is applied for the spatial discretization. Our analysis shows that the proposed scheme is unconditionally stable and has accuracy in time up to third order. We also provide a sophisticated implementation showing that the computational complexity of our schemes is equivalent to solving two linear equations and some algebraic equations. To the best of our knowledge, this is the cheapest unconditionally stable schemes reported in the literature. Some numerical examples are given to verify the efficiency of the proposed method. © 2023, CC BY.

关键词： Anisotropy