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Diagnosis of ultrafast ultraintense laser pulse characteristics by machine-learning-assisted electron spin

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arXiv 2022年

作者： Lu, Zhi-Wei Hou, Xin-Di Wan, Feng Salamin, Yousef I. Lv, Chong Zhang, Bo Wang, Fei Xu, Zhong-Feng Li, Jian-Xing Ministry of Education Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic Devices School of Physics Xi’an Jiaotong University Xi’an710049 China Department of Physics American University of Sharjah POB 26666 Sharjah United Arab Emirates Beijing102413 China Key Laboratory of Plasma Physics Research Center of Laser Fusion China Academy of Engineering Physics Mianshan Rd 64# Sichuan Mianyang621900 China School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China

Rapid development of ultrafast ultraintense laser technologies continues to create opportunities for studying strong-field physics under extreme conditions. However, accurate determination of the spatial and temporal characteristics of a laser pulse is still a great challenge, especially when laser powers higher than hundreds of terawatts are involved. In this paper, by utilizing the radiative spin-flip effect, we find that the spin depolarization of an electron beam can be employed to diagnose characteristics of ultrafast ultraintense lasers with peak intensities around 1020-1022 W/cm2. With three shots, our machine-learning-assisted model can predict, simultaneously, the pulse duration, peak intensity, and focal radius of a focused Gaussian ultrafast ultraintense laser (in principle, the profile can be arbitrary) with relative errors of 0.1%-10%. The underlying physics and an alternative diagnosis method (without the assistance of machine learning) are revealed by the asymptotic approximation of the final spin degree of polarization. Our proposed scheme exhibits robustness and detection accuracy with respect to fluctuations in the electron beam parameters. Accurate measurements of the ultrafast ultraintense laser parameters will lead to much higher precision in, for example, laser nuclear physics investigations and laboratory astrophysics studies. Robust machine learning techniques may also find applications in more general strong-field physics scenarios. © 2022, CC BY.

关键词： Magnetic moments

Discriminating nonisomorphic graphs with an experimental quantum annealer

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Physical Review A 2020年第3期102卷 032622-032622页

作者： Zoe Gonzalez Izquierdo Ruilin Zhou Klas Markström Itay Hen Department of Physics and Astronomy and Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Information Sciences Institute University of Southern California Marina del Rey California 90292 USA Department of Electrical and Computer Engineering Northwestern University Evanston Illinois 60208 USA Department of Mathematics and Mathematical Statistics Umeå University 90187 Umeå Sweden

We demonstrate experimentally the ability of a quantum annealer to distinguish between sets of nonisomorphic graphs that share the same classical Ising spectrum. Utilizing the pause-and-quench features recently introduced into D-Wave quantum annealing processors, which allow the user to probe the quantum Hamiltonian realized in the middle of an anneal, we show that obtaining thermal averages of diagonal observables of “classically indistinguishable” nonisomorphic graphs encoded into transverse-field Ising Hamiltonians enable their discrimination. We discuss the significance of our results in the context of the graph isomorphism problem.

关键词： Quantum computation

Neural-PDE: A RNN based neural network for solving time dependent PDEs

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arXiv 2020年

作者： Hu, Yihao Zhao, Tong Xu, Shixin Lin, Lizhen Xu, Zhiliang Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN46545 United States Department of Computer Science and Engineering University of Notre Dame Notre DameIN46545 United States Duke Kunshan University Jiangsu Kunshan215316 China

Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering. Numerically solving nonlinear and/or high-dimensional PDEs is frequently a challenging task. Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence-to-sequence learning (Seq2Seq) framework called Neural-PDE, which allows one to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the solutions in next n time steps. One critical feature of our proposed framework is that the Neural-PDE is able to simultaneously learn and simulate all variables of interest in a PDE system. We test the Neural-PDE by a range of examples, from one-dimensional PDEs to a multi-dimensional and nonlinear complex fluids model. The results show that the Neural-PDE is capable of learning the initial conditions, boundary conditions and differential operators defining the initial-boundary-value problem of a PDE system without the knowledge of the specific form of the PDE system. In our experiments, the Neural-PDE can efficiently extract the dynamics within 20 epochs training and produce accurate predictions. Furthermore, unlike the traditional machine learning approaches for learning PDEs, such as CNN and MLP, which require great quantity of parameters for model precision, the Neural-PDE shares parameters among all time steps, and thus considerably reduces computational complexity and leads to a fast learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Complex networks

Periodic controls in step 2 strictly-convex sub-Finsler problems

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arXiv 2019年

作者： Sachkov, Yuri L. Yuri Sachkov is Program Systems Institute Pereslavl-Zalessky Russia Department of Mathematics and Statistics University of Jyväskylä Finland

We consider control-linear left-invariant time-optimal problems on step 2 Carnot groups with strictly convex set of control parameters (in particular, sub-Finsler problems). We describe all linear-in-momenta Casimirs on the dual of the Lie algebra. In the case of rank 3 Lie groups we describe the symplectic foliation on the dual of the Lie algebra. On this basis we show that extremal controls are either constant or periodic. Some related results for other Carnot groups are presented. Copyright © 2019, The Authors. All rights reserved.

关键词： Lie groups

Self-Generated Intent-Based System

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Self-Generated Intent-Based System

International Conference on the Network of the Future (NOF)

作者： Mehdi Bezahaf Marco Perez Hernandez Lawrence Bardwell Eleanor Davies Matthew Broadbent Daniel King David Hutchison School of Computing and Communications Lancaster University Department of Engineering University of Cambridge Mathematics and Statistics Lancaster University

ISBN: (数字)9781728144450

ISBN: (纸本)9781728144467

We propose an intent-based system where, on top of the user intentions, the system itself generates suitable Quality of Service and resilience parameters and may augment the intent characteristics if it detects any room for improvement. We demonstrate the feasibility and challenges of such a system using mininet and the ONOS controller.

关键词： Software defined networking Quality of service Detection algorithms Change detection algorithms

Increasing accuracy of classification physical activity based on smartphone using ensemble logistic regression with boosting method 3

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Increasing accuracy of classification physical activity base...

3rd International Conference on Science, ICOS 2019

作者： Lawi, A. Aziz, F. Wungo, S.L. Department of Computer Science Universitas Hasanuddin Makassar90245 Indonesia Faculty of Mathematics and Natural Sciences Universitas Pancasakti Makassar90132 Indonesia Program Study Informatics Engineering STMIK Kreatindo Monokwari98300 Indonesia

At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs etc. Machine learning is needed to group data and get information. Statistical methods have poor performance in classifying because procedures must be met. To overcome this, an ensemble technique was used. This study proposes the application of the gradientboost ensemble method to classify walking upstair and walking downstairs. The *** system is designed for data retrieval using a smartphone. Then, the dataset will be partitioned into 70% training data and 30% test data. The results show that the performance of the ensemble boosting method produces 81.82% accuracy, 86.11% sensitivity and 77.50% specificity. © Published under licence by IOP Publishing Ltd.

关键词： Smartphones

Electric Vehicle Charging Diversity Factors

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Electric Vehicle Charging Diversity Factors

Compatibility in Power Electronics (CPE)

作者： Luiz Artur Pecorelli Peres José Francisco Moreira Pessanha Antonio Guilherme Garcia Lima Windson Braga Pereira Electrical Engineering Department UERJ - State University of Rio de Janeiro (Professor) Rio de Janeiro Brazil UERJ - State University of Rio de Janeiro (Professor) IME - Institute of Mathematics and Statistics Rio de Janeiro Brazil UERJ - State University of Rio de Janeiro (Student) Mechanical Engineering Postgraduate Program Rio de Janeiro Brazil

ISBN: (数字)9781728142180

ISBN: (纸本)9781728142197

This paper presents the determination of diversity factors related to the increased demand when charging EVs, electric vehicles, and focuses on the adequacy of distribution network capacity. The work aimed to meet the R&D project "Smart City Búzios" proposed by ENEL Distribuição Rio within the scope of the National Electric Energy Agency - ANEEL. The treated results of several measurements of EV charging characteristics provided the basis to estimate the increase in electricity demand. Deterministic and stochastic analyzes considering the influence of the distance previously traveled by the vehicle and the moment of its connection to the network were compared, allowed to obtain the diversity factor. This study shows the influence of the connection moment to recharge EVs in predicting the capacity of transformers as well as the need for their stochastic treatment. Therefore, the results recommended implementing communication systems to offer attractive tariffs aiming at shifting the recharging of EVs to daily times of lower demand.

关键词： Batteries Load modeling Electric vehicle charging Standards Reactive power Charge measurement

Impact of Homogeneous-Heterogeneous Reactions on Flow of Non-Newtonian Ferrofluid over a Stretching Sheet

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Journal of Nanomaterials 2022年第1期2022卷

作者： H. Zeb S. Bhatti U. Khan H. A. Wahab A. Mohamed I. Khan Department of Mathematics COMSATS University Islamabad Abbottabad Campus Pakistan University Research Center Future University in Egypt New Cairo 11745 Egyptfue.edu.eg Department of Mathematics & Statistics Hazara University Mansehra 21120 Pakistanhu.edu.pk College of Engineering Majmaah University P.O. Box 66 Majmaah 11952 Saudi Arabiamu.edu.sa

In this study, we considered heterogeneous and homogeneous chemical reactions in Eyring-Powell ferrofluid past a stretchable surface. In addition, thermal radiation and magnetic dipole impacts are also considered. The direct numerical solution of the governing model is complex. For this, we simplified the model into coupled ordinary differential equations (ODEs) with the dimensionless variables. Then, we determined the computational solution of the resulting transformed ODE system via the Runge-Kutta (RK) method. The impacts of interesting engineering parameters on temperature, velocity, and concatenation behaviors are presented through graphs. We concluded that the velocity field is reduced for higher values of ferromagnetic parameter β and fluid material parameters ß and H and arises the temperature field for higher values of thermal radiation R while the temperature field is reduced for Prandtl number Pr and dissipation parameter λ 1 . The obtained results of the current work are compared with published work, and we found that between them a good agreement can be seen in the table. The characteristics of skin fraction and Nusselt number are presented.

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Phase equilibrium of water with hexagonal and cubic ice using the SCAN functional

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arXiv 2021年

作者： Piaggi, Pablo Miguel Panagiotopoulos, Athanassios Z. Debenedetti, Pablo G. Car, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemical and Biological Engineering Princeton University PrincetonNJ08544 United States Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States Department of Physics Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initio accuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye towards studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ice Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic. Copyright © 2021, The Authors. All rights reserved.

关键词： Ice