检索结果-内蒙古大学图书馆

arXiv 2022年

作者： Law, Kody J.H. Walton, Neil Yang, Shangda Jasra, Ajay School of Mathematics University of Manchester ManchesterM13 9PL United Kingdom Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. This setting is ubiquitous across science and engineering applications, for example in the context of Bayesian inference where a physics-based model governed by an intractable partial differential equation (PDE) appears in the likelihood. A multi-index Sequential Monte Carlo (MISMC) method is used to construct ratio estimators which provably enjoy the complexity improvements of multi-index Monte Carlo (MIMC) as well as the efficiency of Sequential Monte Carlo (SMC) for inference. In particular, the proposed method provably achieves the canonical complexity of MSE−1, while single level methods require MSE−ξ for ξ > 1. This is illustrated on examples of Bayesian inverse problems with an elliptic PDE forward model in 1 and 2 spatial dimensions, where ξ = 5/4 and ξ = 3/2, respectively. It is also illustrated on more challenging log Gaussian process models, where single level complexity is approximately ξ = 9/4 and multilevel Monte Carlo (or MIMC with an inappropriate index set) gives ξ = 5/4 + ω, for any ω > 0, whereas our method is again canonical. We also provide novel theoretical verification of the product-form convergence results which MIMC requires for Gaussian processes built in spaces of mixed regularity defined in the spectral domain, which facilitates acceleration with fast Fourier transform methods via a cumulant embedding strategy, and may be of independent interest in the context of spatial statistics and machine learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Layer-dependent optically induced spin polarization in InSe

引用

Physical Review B 2023年第11期107卷 115304-115304页

作者： Jovan Nelson Teodor K. Stanev Dmitry Lebedev Trevor LaMountain J. Tyler Gish Hongfei Zeng Hyeondeok Shin Olle Heinonen Kenji Watanabe Takashi Taniguchi Mark C. Hersam Nathaniel P. Stern Applied Physics Program Northwestern University Evanston Illinois 60208 USA Department of Physics and Astronomy Northwestern University Evanston Illinois 60208 USA Department of Material Science and Engineering Northwestern University Evanston Illinois 60208 USA Computational Science Division Argonne National Laboratory Lemont Illinois 60439 USA Material Science Division Argonne National Laboratory Lemont Illinois 60439 USA Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba 305-0044 Japan Department of Chemistry Northwestern University Evanston Illinois 60208 USA Department of Electrical Engineering and Computer Science Northwestern University Evanston Illinois 60208 USA

Optical control of spin in semiconductors has been pioneered using nanostructures of III-V and II-VI semiconductors, but the emergence of two-dimensional van der Waals materials offers an alternative low-dimensional platform for spintronic phenomena. Indium selenide (InSe), a group-III monochalcogenide van der Waals material, has shown promise for optoelectronics due to its high electron mobility, tunable direct band gap, and quantum transport. In addition to these confirmed properties, there are predictions of spin-dependent optical selection rules, suggesting the potential for all-optical excitation and control of spin in a two-dimensional layered material. Despite these predictions, layer-dependent optical spin phenomena in InSe have yet to be explored. Here, we present measurements of layer-dependent optical spin dynamics in few-layer and bulk InSe. Polarized photoluminescence reveals layer-dependent optical orientation of spin, thereby demonstrating the optical selection rules in few-layer InSe. Spin dynamics are also studied in many-layer InSe using time-resolved Kerr rotation spectroscopy. By applying out-of-plane and in-plane static magnetic fields for polarized emission measurements and Kerr measurements, respectively, the g factor for InSe was extracted. Further investigations are done by calculating precession values using a k·p model, which is supported by ab initio density functional theory. Comparison of predicted precession rates with experimental measurements highlights the importance of excitonic effects in InSe for understanding spin dynamics. Optical orientation of spin is an important prerequisite for optospintronic phenomena and devices, and these first demonstrations of layer-dependent optical excitation of spins in InSe lay the foundation for combining layer-dependent spin properties with advantageous electronic properties found in this material.

关键词： Magneto-optics Spin dynamics Chalcogenides Layered semiconductors Van der Waals systems Density functional theory Kerr effect Photoluminescence k dot p method

来源：评论

学校读者我要写书评

暂无评论

Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

arXiv

引用

arXiv 2022年

作者： Masuda, Naoki Kundu, Prosenjit Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise. © 2022, CC BY.

关键词： Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Early Warnings for Multistage Transitions in Dynamics on Networks

arXiv

引用

arXiv 2022年

作者： MacLaren, Neil G. Kundu, Prosenjit Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multistage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multistage transition. We found that knowledge of both the ongoing multistage transition and network structure enables us to calculate effective early warning signals for multistage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multistage transition, which we systematically characterize. © 2022, CC BY.

关键词： Dynamics

来源：评论

学校读者我要写书评

暂无评论

A Bottom-Up, Multi-Scale Model for Transient Mass Transport of Redox-Active Species Through Porous Electrodes Beyond the Pseudo-Steady Limit

SSRN

引用

SSRN 2022年

作者： Hamid, Md Abdul Smith, Kyle C. Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Materials Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Program University of Illinois at Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States

A transient, multi-scale model is introduced to capture the dynamic response of redox-active electrolyte flowing through porous electrodes under time-dependent current input. Unlike conventional models with time-invariant mass transfer coefficient, certain frequency-dependent transfer functions (TFs) are introduced to embed transient pore-scale transport physics into an up-scaled model. One of the introduced TFs is a spectral Sherwood number, that extends the film law of mass transfer to transient conditions. Another TF is introduced to capture the acceleration/suppression of solute advection resulting from inhomogeneity in local concentration distribution. The frequency dependence of these TFs is demonstrated for electrodes comprised of solid cylinders in crossflow. Three distinct spectral regions are observed, where with increasing frequency a transition from lagless response to semi-infinite Warburg response occurs. The results show orders of magnitude higher mass transfer rate for fast-changing applied current. These TFs are then embedded into an up-scaled model to obtain the time domain response of flow batteries. Analysis of concentration polarization and the average reactant concentration inside electrodes is used to construct non-dimensional maps of their operation space. From this analysis we find that fast current fluctuations can be sustained at conditions exceeding the limiting current that is expected for pseudo-steady operation. © 2022, The Authors. All rights reserved.

关键词： Flow batteries

来源：评论

学校读者我要写书评

暂无评论

Ogden Material Calibration via Magnetic Resonance Cartography, Parameter Sensitivity, and Variational System Identification

arXiv

引用

arXiv 2022年

作者： Nikolov, Denislav P. Srivastava, Siddhartha Abeid, Bachir A. Scheven, Ulrich M. Arruda, Ellen M. Garikipati, Krishna Estrada, Jonathan B. Department of Mechanical Engineering University of Michigan United States Department of Biomedical Engineering University of Michigan United States Program in Macromolecular Science and Engineering University of Michigan United States Department of Mathematics University of Michigan United States Michigan Institute for Computational Discovery and Eng. University of Michigan United States

Contemporary material characterisation techniques that leverage deformation fields and the weak form of the equilibrium equations face challenges in the numerical solution procedure of the inverse characterisation problem. As material models and descriptions differ, so too must the approaches for identifying parameters and their corresponding mechanisms. The widely-used Ogden material model can be comprised of a chosen number of terms of the same mathematical form, which presents challenges of parsimonious representation, interpretability, and stability. Robust techniques for system identification of any material model are important to assess and improve experimental design, in addition to their centrality to forward computations. Using fully 3D displacement fields acquired in silicone elastomers with our recently-developed magnetic resonance cartography (MR-u) technique on the order of > 20, 000 points per sample, we leverage PDE-constrained optimisation as the basis of variational system identification of our material parameters. We incorporate the statistical F-test to maintain parsimony of representation. Using a new, local deformation decomposition locally into mixtures of biaxial and uniaxial tensile states, we evaluate experiments based on an analytical sensitivity metric, and discuss the implications for experimental design. © 2022, CC BY-NC-ND.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel

arXiv

引用

arXiv 2022年

作者： Jiang, Ziyang Zheng, Tongshu Liu, Yiling Carlson, David Department of Civil and Environmental Engineering Duke University United States Division of Natural and Applied Science Duke Kunshan University China Program in Computational Biology and Bioinformatics Duke University School of Medicine United States Department of Civil and Environmental Engineering Department of Biostatistics and Bioinformatics Department of Computer Science Duke University United States

It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kernel in a Gaussian process (GP). Many deep learning applications could be enhanced by modeling such known properties. For example, convolutional neural networks (CNNs) are frequently used in remote sensing, which is subject to strong seasonal effects. We propose to blend the strengths of NNs and the clear modeling capabilities of GPs by using a composite kernel that combines a kernel implicitly defined by a neural network with a second kernel function chosen to model known properties (e.g., seasonality). We implement this idea by combining a deep network and an efficient mapping function based on either Nyström approximation or random Fourier features, which we call Implicit Composite Kernel (ICK). We then adopt a sample-then-optimize approach to approximate the full GP posterior distribution. We demonstrate that ICK has superior performance and flexibility on both synthetic and real-world datasets including a remote sensing dataset. The ICK framework can be used to include prior information into neural networks in many applications. © 2022, CC BY.

关键词： Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

引用

science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

Algal biomass based bio-refineries: Concurrent pre-treatment strategies and perspectives for sustainable feedstock

引用

Biomass and Bioenergy 2024年 187卷

作者： Shashikumar, Uday Ranjan, Kumar Rakesh Sharma, Ankit Naina Subramanian, Balaji Tsai, Pei-Chien Lin, Yuan-Chung Lay, Chyi-How Wang, Chin-Tsan Biswas, Shelly Ponnusamy, Vinoth Kumar Department of Medicinal and Applied Chemistry Kaohsiung Medical University Kaohsiung City 807 Taiwan Department of Chemistry Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida201303 India Department of Mechanical Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Institute of Environmental Engineering National Sun Yat-Sen University Taiwan Master Program of Green Energy Science and Technology Feng Chia University Taiwan Department of Mechanical and Electro-Mechanical Engineering National I-Lan University I Lan Taiwan Department of Space Engineering and Rocketry Birla Institute of Technology Mesra Jharkhand Ranchi835215 India 807 Kaohsiung City Taiwan 807 Kaohsiung City Taiwan Department of Computational Biology Institute of Bioinformatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Institute of Aquatic Science and Technology National Kaohsiung University of Science and Technology Kaohsiung City81157 Taiwan Department of Food Science National Pingtung University of Science and Technology Pingtung91201 Taiwan

The quest for viable and scalable biofuel sources has been at the forefront of scientific innovation for the past three decades. Due to its rich chemical constituents, microalgal biomass has emerged as a pivotal sustainable and scalable feedstock for biorefineries. This comprehensive review critically analyzes the different types of microalgae feedstock, concurrent extraction technologies, bio-pre-treatment procedures, and the key chemical and physical parameters influencing lipid formation and algal biofuel production. We propose a novel approach of photo-initiated culturing of algal biomass using photobioreactors (PBRs) to address the limitations of concurrent space and time-related constraints. The innovative photo bio-refinery strategy presented herein aims to enhance sustainability factors while minimizing emissions, catering to the needs of futuristic non-electric vehicles. A comparative quality analysis of microalgae-derived biofuel against conventional fossil fuels and other biofuels is conducted, considering chemical, environmental, economic, and social perspectives. Furthermore, we elucidate the efficacy of bio-pre-treatment strategies such as dehydration, hydrothermal liquefaction, pyrolysis, and gasification in optimizing biofuel production. The proposed photo biorefineries exhibit the potential to yield a diverse range of value-added products, including biodiesel, biogases, bio-fertilizers, bio-pesticides, bio-alcohols, dyes, proteins, carotenoids, and drug vitals. This review provides a comprehensive framework for the development of sustainable and efficient microalgae-based biorefineries, paving the way for a greener and more economically viable future in the biofuel industry. © 2024 Elsevier Ltd

关键词： Feedstocks

来源：评论

学校读者我要写书评

暂无评论

Linking the polyatomic arrangements of interstitial H2O and cations to bonding within Prussian blue analogues ab initio using gradient-boosted machine learning

引用

Physical Review Materials 2021年第3期5卷 035003-035003页

作者： Sizhe Liu Kyle C. Smith Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Materials Science and Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Computational Science and Engineering Program University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA

Prussian blue analogues (PBAs) are model host compounds for the intercalation of monovalent cations for electrochemical energy storage and separations. However, the interactions among interstitial species and their effects on atomic arrangements therein are understood mainly at a phenomenological level. Analyzing correlations between electronic interactions and polyatomic arrangements in hydrated Prussian blue analogues is complicated by the nonlocal hydrogen-bonding interactions between zeolitic water and framework lattices. Here, we train machine-learning (ML) models to learn DFT-calculated energy landscapes of nickel hexacyanoferrate PBA lattices with various lattice hydration degrees, oxidation states, and types of intercalated alkali cations based on various three-particle feature parameters. This ML approach is enabled by using gradient-boosted regression trees with features that are rotationally invariant geometric parameters. ML model accuracy is shown to be a cation-specific indicator of correlations between energy and polyatomic arrangements. Overlap population analysis among correlated atoms further confirms that such correlations are caused by the competition for dative bonding between Lewis-acid intercalated cations and Lewis bases (cyanide and oxygen in H2O). Examination of lowest-energy structures reveals that cation hydrophilicity and bare ionic radius determine dative-bonding strength, resulting in cation-H2O ordering in interstitial space. The projected energy landscapes of hydrated PBA lattices is also explored in subspaces spanned by certain many-particle feature parameters inspired by ML analysis. The downhill traces in such landscapes indicate that lattice distortion is accompanied by two kinds of collective movements: (1) rearrangements in the hydration shells around small and hydrophilic cations and (2) collective attack of H2O molecules on nickel-cyanide bonds promoted by large, hydrophobic cations.

关键词： Chemical binding Density functional theory Electrochemistry Electron correlation calculations for atoms & ions Electronic structure of atoms & molecules Energy storage Interatomic & molecular potentials Organic & organometallic chemistry Water Data mining Electronic structure Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：