arXiv: Computational Physics最新文献_第3页

Neural network solutions to differential equations in nonconvex domains: Solving the electric field in the slit-well microfluidic device 非凸域微分方程的神经网络解法:求解狭缝井微流控装置中的电场

arXiv: Computational Physics Pub Date : 2020-04-25 DOI: 10.1103/PHYSREVRESEARCH.2.033110

M. Magill, Andrew M. Nagel, H. D. de Haan

{"title":"Neural network solutions to differential equations in nonconvex domains: Solving the electric field in the slit-well microfluidic device","authors":"M. Magill, Andrew M. Nagel, H. D. de Haan","doi":"10.1103/PHYSREVRESEARCH.2.033110","DOIUrl":"https://doi.org/10.1103/PHYSREVRESEARCH.2.033110","url":null,"abstract":"The neural network method of solving differential equations is used to approximate the electric potential and corresponding electric field in the slit-well microfluidic device. The device's geometry is non-convex, making this a challenging problem to solve using the neural network method. To validate the method, the neural network solutions are compared to a reference solution obtained using the finite element method. Additional metrics are presented that measure how well the neural networks recover important physical invariants that are not explicitly enforced during training: spatial symmetries and conservation of electric flux. Finally, as an application-specific test of validity, neural network electric fields are incorporated into particle simulations. Conveniently, the same loss functional used to train the neural networks also seems to provide a reliable estimator of the networks' true errors, as measured by any of the metrics considered here. In all metrics, deep neural networks significantly outperform shallow neural networks, even when normalized by computational cost. Altogether, the results suggest that the neural network method can reliably produce solutions of acceptable accuracy for use in subsequent physical computations, such as particle simulations.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81252994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

The Maxwell–Stefan Diffusion Limit of a Hard-Sphere Kinetic Model for Mixtures 混合硬球动力学模型的Maxwell-Stefan扩散极限

arXiv: Computational Physics Pub Date : 2020-04-23 DOI: 10.1007/978-3-030-69784-6_2

B. Anwasia

引用次数: 1

Fast and stable determinant quantum Monte Carlo 快速稳定的行列式量子蒙特卡罗

arXiv: Computational Physics Pub Date : 2020-03-11 DOI: 10.21468/SCIPOSTPHYSCORE.2.2.011

C. Bauer

引用次数: 4

A symplectic integrator for molecular dynamics on a hypersphere 超球上分子动力学的辛积分

arXiv: Computational Physics Pub Date : 2020-03-05 DOI: 10.5488/cmp.23.23603

J. Caillol

引用次数: 0

Numerical Discretization of Variational Phase Field Model for Phase Transitions in Ferroelectric Thin Films 铁电薄膜相变变分相场模型的数值离散化

arXiv: Computational Physics Pub Date : 2020-03-01 DOI: 10.4208/CICP.OA-2020-0118

Ruo Li, Q. Du, Lei Zhang

引用次数: 0

Traps for pinning and scattering of antiferromagnetic skyrmions via magnetic properties engineering 磁性工程技术用于反铁磁粒子的钉住和散射陷阱

arXiv: Computational Physics Pub Date : 2020-02-28 DOI: 10.1063/5.0006219

D. Toscano, I. A. Santece, R. Guedes, H. S. Assis, A. Miranda, C. D. de Araújo, F. Sato, P. Coura, S. A. Leonel

{"title":"Traps for pinning and scattering of antiferromagnetic skyrmions via magnetic properties engineering","authors":"D. Toscano, I. A. Santece, R. Guedes, H. S. Assis, A. Miranda, C. D. de Araújo, F. Sato, P. Coura, S. A. Leonel","doi":"10.1063/5.0006219","DOIUrl":"https://doi.org/10.1063/5.0006219","url":null,"abstract":"Micromagnetic simulations have been performed to investigate the controllability of the skyrmion position in antiferromagnetic nanotracks with their magnetic properties modified spatially. In this study we have modeled magnetic defects as local variations on the material parameters, such as the exchange stiffness, saturation magnetization, perpendicular magnetocrystalline anisotropy and Dzyaloshinskii-Moriya constant. Thus, we have observed not only pinning (potential well) but also scattering (potential barrier) of antiferromagnetic skyrmions, when adjusting either a local increase or a local reduction for each material parameter. In order to control of the skyrmion motion it is very important to impose certain positions along the nanotrack where the skyrmion can stop. Magnetic defects incorporated intentionally in antiferromagnetic racetracks can be useful for such purpose. In order to provide guidelines for experimental studies, we vary both material parameters and size of the modified region. The found results show that the efficiency of skyrmion trap depends on a suitable combination of magnetic defect parameters. Furthermore, we discuss the reason why skyrmions are either attracted or repelled by a region magnetically modified.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75323856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging 带激发扫描探针显微镜成像中最优动态模型选择的贝叶斯推理

arXiv: Computational Physics Pub Date : 2020-02-19 DOI: 10.1063/5.0005323

R. Vasudevan, K. Kelley, E. Eliseev, S. Jesse, H. Funakubo, A. Morozovska, Sergei V. Kalinin

{"title":"Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging","authors":"R. Vasudevan, K. Kelley, E. Eliseev, S. Jesse, H. Funakubo, A. Morozovska, Sergei V. Kalinin","doi":"10.1063/5.0005323","DOIUrl":"https://doi.org/10.1063/5.0005323","url":null,"abstract":"The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e. conversion from detected signal to descriptors specific to tip-surface interactions and subsequently to materials properties. Here, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation (BE) SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We note that in application of Bayesian methods, special care should be made for proper setting on the problem as model selection vs. establishing practical parameter equivalence. We further explore the non-linear mechanical behaviors at topological defects in a classical ferroelectric material, PbTiO3. We observe the non-trivial evolution of Duffing resonance frequency and the nonlinearity of the sample surface, suggesting the presence of the hidden elements of domain structure. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls can be significantly broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81256914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Neural network representability of fully ionized plasma fluid model closures 全电离等离子体流体模型闭包的神经网络可表征性

arXiv: Computational Physics Pub Date : 2020-02-10 DOI: 10.1063/5.0006457

R. Maulik, N. Garland, Xianzhu Tang, Prasanna Balaprakash

引用次数: 17

Topological quantum phase transitions retrieved through unsupervised machine learning 通过无监督机器学习检索拓扑量子相变

arXiv: Computational Physics Pub Date : 2020-02-06 DOI: 10.1103/physrevb.102.134213

Yanming Che, C. Gneiting, Tao Liu, F. Nori

{"title":"Topological quantum phase transitions retrieved through unsupervised machine learning","authors":"Yanming Che, C. Gneiting, Tao Liu, F. Nori","doi":"10.1103/physrevb.102.134213","DOIUrl":"https://doi.org/10.1103/physrevb.102.134213","url":null,"abstract":"The discovery of topological features of quantum states plays an important role in modern condensed matter physics and various artificial systems. Due to the absence of local order parameters, the detection of topological quantum phase transitions remains a challenge. Machine learning may provide effective methods for identifying topological features. In this work, we show that the unsupervised manifold learning can successfully retrieve topological quantum phase transitions in momentum and real space. Our results show that the Chebyshev distance between two data points sharpens the characteristic features of topological quantum phase transitions in momentum space, while the widely used Euclidean distance is in general suboptimal. Then a diffusion map or isometric map can be applied to implement the dimensionality reduction, and to learn about topological quantum phase transitions in an unsupervised manner. We demonstrate this method on the prototypical Su-Schrieffer-Heeger (SSH) model, the Qi-Wu-Zhang (QWZ) model, and the quenched SSH model in momentum space, and further provide implications and demonstrations for learning in real space, where the topological invariants could be unknown or hard to compute. The interpretable good performance of our approach shows the capability of manifold learning, when equipped with a suitable distance metric, in exploring topological quantum phase transitions.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83115464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

Physical Extrapolation of Quantum Observables by Generalization with Gaussian Processes 用高斯过程泛化量子可观测量的物理外推

arXiv: Computational Physics Pub Date : 2020-01-01 DOI: 10.1007/978-3-030-40245-7_9

R. A. Vargas-Hernández, R. Krems

引用次数: 3