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Physical Review C 2023年第2期107卷 024606-024606页

作者： Yijia Qiu Changlin Lan Yonghao Chen Liyang Jiang Jie Bao Yiwei Yang Zhongwei Wen Rong Liu Xichao Ruan Jingyu Tang Jie Ren Hantao Jing Guangyuan Luan Ruirui Fan Yangbo Nie Xianlin Yang Xiaojun Li Han Yi Wei Jiang Tao Ye Yi Yang Shilong Liu Jincheng Wang School of Nuclear Science and Technology Lanzhou University Lanzhou 730000 China Institute of High Energy Physics Chinese Academy of Sciences (CAS) Beijing 100049 China Spallation Neutron Source Science Center Dongguan 523803 China Key Laboratory of Nuclear Data China Institute of Atomic Energy Beijing 102413 China Institute of Nuclear Physics and Chemistry China Academy of Engineering Physics Mianyang 621900 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China

The neutron-induced fission cross section of Pu239 was measured relative to U235(n,f) at the back-streaming white neutron beam line (Back-n) of the China Spallation Neutron Source (CSNS). A multicell fast fission ionization chamber was used to perform the measurement. The reliability of the measurement was verified by the high consistency of U235's resonances between the measurement and the evaluation data. The Pu239 fission cross sections from 4keV to 100MeV are obtained with 1.7–5.8 % uncertainty when unfolding uncertainties are excluded. The total uncertainties including the unfolding errors, which reflect the effect of the double-bunch unfolding method, are 2.6–15 % from 10keV to 100MeV.

关键词： Fission A ≥ 220 Spectrometers & spectroscopic techniques

Improving hp-Variational physics-Informed Neural Networks for Steady-State Convection-Dominated Problems

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arXiv 2024年

作者： Anandh, Thivin Ghose, Divij Jain, Himanshu Sunkad, Pratham Ganesan, Sashikumaar John, Volker Department of Computational and Data Sciences Indian Institute of Science Karnataka Bangalore India Department of Mechanical Engineering Indian Institute of Technology Tamil Nadu Madras India Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Department of Mathematics and Computer Science Freie University Berlin Germany

This paper proposes and studies two extensions of applying hp-variational physics-informed neural networks, more precisely the FastVPINNs framework, to convection-dominated convection-diffusion-reaction problems. First, a term in the spirit of a SUPG stabilization is included in the loss functional and a network architecture is proposed that predicts spatially varying stabilization parameters. Having observed that the selection of the indicator function in hard-constrained Dirichlet boundary conditions has a big impact on the accuracy of the computed solutions, the second novelty is the proposal of a network architecture that learns good parameters for a class of indicator functions. Numerical studies show that both proposals lead to noticeably more accurate results than approaches that can be found in the literature. © 2024, CC BY-NC-SA.

关键词： Stabilization

Detection of the chirality of twisted bilayer graphene by the optical absorption

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Physical Review B 2024年第12期109卷 125419-125419页

作者： Xin-Miao Qiu Ning Yang Weidong Chu Jie-Yun Yan School of Science Beijing University of Posts and Telecommunications Beijing 100876 China Institute of Applied Physics and Computational Mathematics Beijing 100088 China National Key Laboratory of Computational Physics Beijing 100088 China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing 100876 China

Twisted bilayer graphene possesses intrinsic chirality, which has received less attention among the extensive research conducted on twistronics. In this paper, the optical absorption theory beyond the common linear perturbation is developed with more sophisticated light-electron interactions considered. Numerical calculations based on the theory found that reducing the twisted angle produces a redshift of absorption in the high-frequency range, while increasing absorption strength in the low-frequency domain, which can be further significantly enhanced when applying an interlayer bias. More importantly, the absorption spectroscopy is shown to be an effective way to detect both the chirality and the anisotropy of the twisted bilayer structure.

关键词： Twistronics Bilayer graphene Optical absorption spectroscopy Tight-binding model

Training Guarantees of Neural Network Classification Two-Sample Tests by Kernel Analysis

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arXiv 2024年

作者： Khurana, Varun Cheng, Xiuyuan Cloninger, Alexander Department of Applied Mathematics Brown University ProvidenceRI02906 United States Department of Mathematics Duke University DurhamNC27708 United States Department of Mathematics Halicioglu Data Science Institute University of California San Diego San DiegoCA92093 United States

We construct and analyze a neural network two-sample test to determine whether two datasets came from the same distribution (null hypothesis) or not (alternative hypothesis). We perform time-analysis on a neural tangent kernel (NTK) two-sample test. In particular, we derive the theoretical minimum training time needed to ensure the NTK two-sample test detects a deviation-level between the datasets. Similarly, we derive the theoretical maximum training time before the NTK two-sample test detects a deviation-level. By approximating the neural network dynamics with the NTK dynamics, we extend this time-analysis to the realistic neural network two-sample test generated from time-varying training dynamics and finite training samples. A similar extension is done for the neural network two-sample test generated from time-varying training dynamics but trained on the population. To give statistical guarantees, we show that the statistical power associated with the neural network two-sample test goes to 1 as the neural network training samples and test evaluation samples go to infinity. Additionally, we prove that the training times needed to detect the same deviation-level in the null and alternative hypothesis scenarios are well-separated. Finally, we run some experiments showcasing a two-layer neural network two-sample test on a hard two-sample test problem and plot a heatmap of the statistical power of the two-sample test in relation to training time and network complexity. © 2024, CC BY.

关键词： Network layers

How graph neural networks learn: lessons from training dynamics 24

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How graph neural networks learn: lessons from training dynam...

Proceedings of the 41st International Conference on Machine Learning

作者： Chenxiao Yang Qitian Wu David Wipf Ruoyu Sun Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI Shanghai Jiao Tong University Amazon Web Services School of Data Science The Chinese University of Hong Kong Shenzhen and Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data

A long-standing goal in deep learning has been to characterize the learning behavior of black-box models in a more interpretable manner. For graph neural networks (GNNs), considerable advances have been made in formalizing what functions they can represent, but whether GNNs will learn desired functions during the optimization process remains less clear. To fill this gap, we study their training dynamics in function space. In particular, we find that the gradient descent optimization of GNNs implicitly leverages the graph structure to update the learned function, as can be quantified by a phenomenon which we call kernel-graph alignment. We provide theoretical explanations for the emergence of this phenomenon in the overparameterized regime and empirically validate it on real-world GNNs. This finding offers new interpretable insights into when and why the learned GNN functions generalize, highlighting their limitations in heterophilic graphs. Practically, we propose a parameter-free algorithm that directly uses a sparse matrix (i.e. graph adjacency) to update the learned function. We demonstrate that this embarrassingly simple approach can be as effective as GNNs while being orders-of-magnitude faster.

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A multiphase-field model for simulating the hydrogen-induced multi-spot corrosion on the surface of polycrystalline metals: Application to uranium metal

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arXiv 2023年

作者： Sheng, Jie Liu, Yu Shi, Xiao-Ming Wang, Yue-Chao Chen, Zi-Hang Xu, Ke Wu, Shuai Huang, Hou-Bing Sun, Bo Liu, Hai-Feng Song, Hai-Feng Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing100088 China School of Materials Science and Engineering Beijing Institute of Technology Beijing100081 China Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China

Hydrogen-induced multi-spot corrosion on the surface of polycrystalline raremetals is a complex process, which involves the interactions between phases(metal, hydride and oxide), grain orientations, grain boundaries, and corrosionspots. To accurately simulate this process and comprehend the underlyingphysics, a theoretical method is required that includes the followingmechanisms: i) hydrogen diffusion, ii) phase transformation, iii) elasticinteractions between phases, especially, the interactions between the oxidefilm and the hydride, iv) elastic interactions between grains, and v)interactions between hydrogen solutes and grain boundaries. In this study, wereport a multiphase-field model that incorporates all these requirements, andconduct a comprehensive study of hydrogen-induced spot corrosion on the uraniummetal surface, including the investigation of the oxide film, multi-spotcorrosion, grain orientation, and grain boundary in the monocrystal, bicrystal,and polycrystal systems. The results indicate that the oxide film can inhibitthe growth of hydrides and plays a crucial role in determining the correctmorphology of the hydride at the triple junction of phases. The elasticinteraction between multiple corrosion spots causes the merging of corrosionspots and promotes the growth of hydrides. The introduction of grainorientations and grain boundaries results in a variety of intriguingintracrystalline and intergranular hydride morphologies. The model presentedhere is generally applicable to the hydrogen-induced multi-spot corrosion onany rare metal surface. Copyright © 2023, The Authors. All rights reserved.

关键词： Hydrides

A New Scalar Auxiliary Variable Approach for Gradient Flows

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arXiv 2024年

作者： Zhang, Jinpeng Wang, Xiaoping Department of Mathematics University of Macau China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong518172 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong

The scalar auxiliary variable (SAV) approach [1] is a highly efficient method widely used for solving gradient flow systems. This approach offers several advantages, including linearity, unconditional energy stability, and ease of implementation. By introducing scalar auxiliary variables, a modified system that is equivalent to the original system is constructed at the continuous level. However, during temporal discretization, computational errors can lead to a loss of equivalence and accuracy. In this paper, we introduce a new Constant Scalar Auxiliary Variable (CSAV) approach in which we derive an Ordinary Differential Equation (ODE) for the constant scalar auxiliary variable r. We also introduce a stabilization parameter (α) to improve the stability of the scheme by slowing down the dynamics of r. The CSAV approach provides additional benefits as well. We explicitly discretize the auxiliary variable in combination with the nonlinear term, enabling the solution of a single linear system with constant coefficients at each time step. This new approach also eliminates the need for assumptions about the free energy potential, removing the bounded-from-below restriction imposed by the nonlinear free energy potential in the original SAV approach. Finally, we validate the proposed method through extensive numerical simulations, demonstrating its effectiveness and accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Ordinary differential equations

A fully-decoupled second-order-in-time and unconditionally energy stable scheme for a phase-field model of two phase flow with variable density

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arXiv 2024年

作者： Zhang, Jinpeng Luo, Li Wang, Xiaoping Department of Mathematics University of Macau China School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong 518172 China Department of Mathematics The Hong Kong University of Science and Technology Hong Kong

In this paper, we develop a second-order, fully decoupled, and energy-stable numerical scheme for the Cahn-Hilliard-Navier-Stokes model for two phase flow with variable density and viscosity. We propose a new decoupling Constant Scalar Auxiliary Variable (D-CSAV) method which is easy to generalize to schemes with high order accuracy in time. The method is designed using the "zero-energy-contribution" property while maintaining conservative time discretization for the "non-zero-energy-contribution" terms. Our algorithm simplifies to solving three independent linear elliptic systems per time step, two of them with constant coefficients. The update of all scalar auxiliary variables is explicit and decoupled from solving the phase field variable and velocity field. We rigorously prove unconditional energy stability of the scheme and perform extensive benchmark simulations to demonstrate accuracy and efficiency of the method. Copyright © 2024, The Authors. All rights reserved.

关键词： Navier Stokes equations

Is machine learning good or bad for the natural sciences?

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arXiv 2024年

作者： Hogg, David W. Villar, Soledad Center for Cosmology and Particle Physics Department of Physics New York University United States Max-Planck-Institut für Astronomie Heidelberg Germany Flatiron Institute New York United States Department of Applied Mathematics and Statistics Johns Hopkins University United States Mathematical Institute for Data Science Johns Hopkins University United States

Machine learning (ML) methods are having a huge impact across all of the sciences. However, ML has a strong ontology—in which only the data exist—and a strong epistemology—in which a model is considered good if it performs well on held-out training data. These philosophies are in strong conflict with both standard practices and key philosophies in the natural sciences. Here we identify some locations for ML in the natural sciences at which the ontology and epistemology are valuable. For example, when an expressive machine learning model is used in a causal inference to represent the effects of confounders, such as foregrounds, backgrounds, or instrument calibration parameters, the model capacity and loose philosophy of ML can make the results more trustworthy. We also show that there are contexts in which the introduction of ML introduces strong, unwanted statistical biases. For one, when ML models are used to emulate physical (or first-principles) simulations, they amplify confirmation biases. For another, when expressive regressions are used to label datasets, those labels cannot be used in downstream joint or ensemble analyses without taking on uncontrolled biases. The question in the title is being asked of all of the natural sciences;that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields;the (partial) answers we give here come from the particular perspective of physics. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

Strain Rate Effects of Dynamic-Driven High Dislocation Densities in the Ultra-High Strength Ferrium S53 Steel

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SSRN

SSRN 2024年

作者： Zheng, Shujing Liu, Xiaoru Fang, Leiming Xiao, Likang Guo, Liang Feng, Huiyu Liu, Qian Xiong, Zhengwei Gao, Zhipeng Zhu, Wenkun Joint Laboratory for Extreme Conditions Matter Properties School of Mathematics and Physics Southwest University of Science and Technology China Institute of Physics Nuclear and Chemistry China Academy of Engineering Physics Mianyang621900 China Southwest Institute of Applied Magnetism Mianyang621010 China Institute of Fluid Physics China Academy of Engineering Physics Mianyang621900 China Southwest University of Science and Technology China

Ultra-high strength steels achieve exceptional strength and toughness through precipitation strengthening, of which the properties and structure have absorbed lots of attentions, especially the dislocations could significantly impact the mechanical properties and deformation behaviors. However, the rate effect of deformation under pressing were little studied due to extensive dislocations that limits plasticity. Herein, we investigated the strain rate effects of deformation behaviors on Ferrium S53 steel, a severely deformed ultra-high strength alloy with a higher initial dislocation density than aging hardening steels, across a broad range of strain rates (0.1 s-1[[EQUATION]] 104 s-1). The results revealed both the yield strength and yield drop of Ferrium S53 steel increased progressively with higher strain rates at the yield stage. The strain rate sensitivity index was lower than other reported ultra-high strength steels, attributed to its high dislocation density. During the hardening stage, Ferrium S53 steel exhibited a stronger strain rate effect on flow stress than other high-strength steels, as described by the modified Johnson-Cook constitutive model. The behavior is explained by the transitions of dislocations to low-angle grain boundaries and high-angle grain boundaries under varying strain rates. The exploration of the "strain rate-structure-performance" relationship provides valuable data to guide the application and design of materials with high dislocation density structures. © 2024, The Authors. All rights reserved.

关键词： Strain rate