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Hamiltonian Hopping for Efficient Chiral Mode Switching in Encircling Exceptional Points

Physical Review Letters 2020年第18期125卷 187403-187403页

作者： Aodong Li Jianji Dong Jian Wang Ziwei Cheng John S. Ho Dawei Zhang Jing Wen Xu-Lin Zhang C. T. Chan Andrea Alù Cheng-Wei Qiu Lin Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore 117583 Singapore Engineering Research Center of Optical Instrument and Systems Ministry of Education and Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology No. 516 Jungong Road Shanghai 200093 China State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun 130012 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong 999077 China Photonics Initiative Advanced Science Research Center City University of New York New York New York 10031 USA Physics Program Graduate Center City University of New York New York New York 10016 USA

Dynamically encircling exceptional points (EPs) can lead to chiral mode switching as the system parameters are varied along a path that encircles EP. However, conventional encircling protocols result in low transmittance due to path-dependent losses. Here, we present a paradigm to encircle EPs that includes fast Hamiltonian variations on the parameter boundaries, termed Hamiltonian hopping, enabling ultrahigh-efficiency chiral mode switching. This protocol avoids path-dependent loss and allows us to experimentally demonstrate nearly 90% efficiency at 1550 nm in the clockwise direction, overcoming a long-standing challenge of non-Hermitian optical systems and powering up new opportunities for EP physics.

关键词： Open quantum systems & decoherence PT-symmetric quantum mechanics Photonics Waveguides

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Student Space Missions – Facilitating Pathways to Success for Next Generation Professionals in Space

Student Space Missions – Facilitating Pathways to Success f...

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IAF Space Education and Outreach Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Bering, Edgar A. Khan, Shuhab Moges, Mequanint Hampton, Donald Mölders, Nicole Thorsen, Denise Dunbar, Bonnie J. Lefer, Barry L. Gamblin, Rachel B. Greer, Michael Greer, Presley Gunawan, Bryan Hernandez, Elizabeth Lehnen, Jamie Nguyencuu, Andy Piña, Megan Porat, Itay Prince, John R. Pessoa, Ana Gabriela Simmons, James Tovar, Chloe Ulinski, Alexandra Departments of Physics and Electrical Engineering University of Houston 3507 Cullen Blvd. #617 HoustonTX77204-5005 United States Department of Earth and Atmospheric Sciences University of Houston 3507 Cullen Blvd. #312 HoustonTX77204-5007 United States Department of Engineering Technology 4730 Calhoun Road #304 HoustonTX77204-4020 United States Geophysical Institute University of Alaska Fairbanks 2156 Koyukuk Drive FairbanksAK99775 United States Department of Aerospace Engineering Texas A&M University 701 H.R. Bright Bldg. #609B College StationTX77843-3141 United States Tropospheric Composition Program Earth Sciences Division NASA Headquarters 300 E St SW WashingtonDC20024 United States NASA Johnson Space Center Cimarron Software Services Inc. 2101 NASA Parkway HoustonTX77058 United States Alumni University of Houston 3507 Cullen Blvd. HoustonTX77204 United States UT Southwestern Medical Center 5323 Harry Hines Blvd DallasTX75390 United States Department of Computer Science University of Houston 3551 Cullen Blvd. #501 HoustonTX77204-3010 United States University of Pennsylvania School of Design 210 S. 34th St. PhiladelphiaPA19104 United States

ISBN: (纸本)9781713843177

The Undergraduate Student Instrumentation Project (USIP) was a NASA program created to engage undergraduate students in rigorous scientific research for the purposes of innovation and developing the next generation of professionals in space research. It is now run by the University of Houston using local resources. This student-led project, based on the 5E instructional model, is executed by the students from initial ideation of research objectives to the design, testing, and deployment of scientific payloads. The 5E Instructional model places the student at the center of knowledge building, while instructors facilitate interaction with content and guide the inquiry process. Since 2013, this project has been not only an effective vehicle for delivering STEM education but is also effective in increasing classroom engagement and interest in space. Space research is inherently interdisciplinary and crosscuts Geoscience, engineering, and technology. The project is designed to integrate engineering, technology, physics, material science, and earth and atmospheric sciences as an important opportunity for the students to gain access to cross-disciplinary experiential research. In addition to classroom engagement, the students build their own payloads and ground instruments. This project increases students’ command of essential skills such as teamwork, collaboration, problem solving, technology, communication, innovation, and leadership. For the faculty, the project was an extended exercise in professional development, learning how to implement project level inquiry-based education on this scale. For the students, this formative experience continues to encourage the development of a much broader range of technical skills than is typically offered within an undergraduate degree. Furthermore, the extensive time and energy that students commit to this project promotes a strong sense of personal and professional responsibility and emphasizes the necessity of coherent teamwork.

关键词： Students

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Extended Drude Model for Intraband-Transition-Induced Optical Nonlinearity

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Physical Review Applied 2019年第6期11卷 064062-064062页

作者： Heng Wang Kang Du Chuhao Jiang Zhiqiang Yang Lixia Ren Wending Zhang Soo Jin Chua Ting Mei MOE Key Laboratory of Material Physics and Chemistry under Extraordinary Conditions and Shaanxi Key Laboratory of Optical Information Technology School of Science Northwestern Polytechnical University Xi’an 710072 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 LEES Program Singapore-MIT Alliance for Research & Technology (SMART) 1 CREATE Way #10-01 CREATE Tower Singapore 138602

No existing model has completely explained the physics of the optical nonlinearity for epsilon-near-zero materials, which have recently emerged as candidates for design and fabrication of active nanophotonic devices. We propose to extend the Drude model by adopting a weighted electron effective mass, which takes into account the electron distribution in the nonparabolic conduction band. The extended Drude model is able to interpret the cause of the nonlinearity and provide a functional relationship between the refractive index and wavelength. Both the band nonparabolicity and the temperature-dependent mobility are responsible for the intraband-transition-induced optical nonlinearity. The spectrally resolved nonlinear refractive index and nonlinear susceptibilities are obtained from this model. The results can be applied to other transparent conducting oxides and will help in the design and modeling of spectral responses of nonlinear plasmonic devices.

关键词： Electronic transitions Nonlinear optical susceptibility Optoelectronics Plasmonics Ultrafast optics Thin films Free-electron model Ultrafast pump-probe spectroscopy

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A single-phase distributed generation system with load power compensation capability using linear quadratic regulator 13

A single-phase distributed generation system with load power...

引用

13th IEEE International Conference on Industry Applications, INDUSCON 2018

作者： Fogli, Gabriel A. De Fernandes, Marcelo C. De Faria, Jose A. T. Barbosa, Pedro G. De Almeida, Pedro M. Department of Electrical Engineering Federal University of Ouro Preto João Monlevade MG Brazil Power Electronics and Automation Group Electrical Engineering Program Federal University of Juiz de Fora MG Brazil Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States Flextronics Institute of Technology Sorocaba SP Brazil

ISBN: (纸本)9781538679951

This paper presents design steps of a distributed generation system with load power compensation. A single-phase full-bridge converter is used to connect the alternative source to the mains. Besides injecting active power, the distributed generation also provides load power compensation capability. In this context, a current reference strategy based on single-phase circuit theory is proposed for grid-connected multi-task converters. The main idea is to synthesize the signal reference for control loop generating orthogonal signals from grid voltage and current load. A Third Order Sinusoidal Integrator is used to extract fundamental component and generate the orthogonal waveforms of grid voltage and load current. Current controller loop for interface converter is developed based on Linear Quadratic Regulator theory. Simulation results are presented to verify the system behavior. © 2018 IEEE.

关键词： Distributed power generation

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Effect of Zeeman coupling on the Majorana vortex modes in iron-based topological superconductors

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Physical Review B 2020年第2期101卷 020504(R)-020504(R)页

作者： Areg Ghazaryan P. L. S. Lopes Pavan Hosur Matthew J. Gilbert Pouyan Ghaemi IST Austria (Institute of Science and Technology Austria) Am Campus 1 3400 Klosterneuburg Austria Stewart Blusson Quantum Matter Institute University of British Columbia Vancouver British Columbia Canada V6T 1Z4 Department of Physics University of Houston Houston Texas 77204 USA Micro and Nanotechnology Laboratory University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Department of Electrical Engineering Stanford University Stanford California 94305 USA Physics Department City College of the City University of New York New York New York 10031 USA Physics Program Graduate Center of the City University of New York New York 10031 USA

In the superconducting regime of FeTe(1−x)Sex, there exist two types of vortices which are distinguished by the presence or absence of zero-energy states in their core. To understand their origin, we examine the interplay of Zeeman coupling and superconducting pairings in three-dimensional metals with band inversion. Weak Zeeman fields are found to suppress intraorbital spin-singlet pairing, known to localize the states at the ends of the vortices on the surface. On the other hand, an orbital-triplet pairing is shown to be stable against Zeeman interactions, but leads to delocalized zero-energy Majorana modes which extend through the vortex. In contrast, the finite-energy vortex modes remain localized at the vortex ends even when the pairing is of orbital-triplet form. Phenomenologically, this manifests as an observed disappearance of zero-bias peaks within the cores of topological vortices upon an increase of the applied magnetic field. The presence of magnetic impurities in FeTe(1−x)Sex, which are attracted to the vortices, would lead to such Zeeman-induced delocalization of Majorana modes in a fraction of vortices that capture a large enough number of magnetic impurities. Our results provide an explanation for the dichotomy between topological and nontopological vortices recently observed in FeTe(1−x)Sex.

关键词： Majorana bound states Topological superconductors Iron-based superconductors

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The bulk van der Waals layered magnet CrSBr is a quasi-1D material

arXiv

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arXiv 2022年

作者： Klein, Julian Pingault, Benjamin Florian, Matthias Heißenbüttel, Marie-Christin Steinhoff, Alexander Song, Zhigang Torres, Kierstin Dirnberger, Florian Curtis, Jonathan B. Weile, Mads Penn, Aubrey Deilmann, Thorsten Dana, Rami Bushati, Rezlind Quan, Jiamin Luxa, Jan Sofer, Zdenek Alù, Andrea Menon, Vinod M. Wurstbauer, Ursula Rohlfing, Michael Narang, Prineha Lončar, Marko Ross, Frances M. Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States QuTech Delft University of Technology Delft2600 GA Netherlands Department of Electrical and Computer Engineering Department of Physics University of Michigan Ann ArborMI48109 United States Institut für Festkörpertheorie Westfälische Wilhelms-Universität Münster Münster48149 Germany Institut für Theoretische Physik Universität Bremen P.O. Box 330 440 Bremen28334 Germany Bremen Center for Computational Materials Science University of Bremen Bremen28359 Germany Department of Physics City College of New York New YorkNY10031 United States College of Letters and Science UCLA Los AngelesCA90095 United States Department of Physics Technical University of Denmark Kgs. LyngbyDK-2800 Denmark MIT.nano Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics The Graduate Center City University of New York New YorkNY10016 United States Photonics Initiative CUNY Advanced Science Research Center New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10026 United States Department of Inorganic Chemistry University of Chemistry and Technology Prague Technická 5 Prague 6166 28 Czech Republic Institute of Physics Center for Nanotechnology University of Münster Münster48149 Germany

Correlated quantum phenomena in one-dimensional (1D) systems that exhibit competing electronic and magnetic order are of strong interest for studying fundamental interactions and excitations, such as Tomonaga-Luttinger liquids and topological orders and defects with properties completely different from the quasiparticles expected in their higher-dimensional counterparts. However, clean 1D electronic systems are difficult to realize experimentally, particularly magnetically ordered systems. Here, we show that the van der Waals layered magnetic semiconductor CrSBr behaves like a quasi-1D material embedded in a magnetically ordered environment. The strong 1D electronic character originates from the Cr-S chains and the combination of weak interlayer hybridization and anisotropy in effective mass and dielectric screening with an effective electron mass ratio of meX/meY ∼ 50. This extreme anisotropy experimentally manifests in strong electron-phonon and exciton-phonon interactions, a Peierls-like structural instability and a Fano resonance from a van Hove singularity of similar strength of metallic carbon nanotubes. Moreover, due to the reduced dimensionality and interlayer coupling, CrSBr hosts spectrally narrow (1 meV) excitons of high binding energy and oscillator strength that inherit the 1D character. Overall, CrSBr is best understood as a stack of weakly hybridized monolayers and appears to be an experimentally attractive candidate for the study of exotic exciton and 1D correlated many-body physics in the presence of magnetic order. Copyright © 2022, The Authors. All rights reserved.

关键词： Excitons

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Single Layer & Multi-layer Long Short-Term Memory (LSTM) Model with Intermediate Variables for Weather Forecasting 3

Single Layer & Multi-layer Long Short-Term Memory (LSTM) Mod...

引用

3rd International Conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Salman, Afan Galih Heryadi, Yaya Abdurahman, Edi Suparta, Wayan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Engineering Faculty of Science and Technology Sanata Dharma University Yogyakarta55282 Indonesia

Weather forecasting has gained attention many researchers from various research communities due to its effect to the global human life. The emerging deep learning techniques in the last decade coupled and the wide availability of massive weather observation data have motivated many researches to explore hidden hierarchical pattern in the large volume of weather dataset for weather forecasting. The purposes of this research are to build a robust and adaptive statistical model for forecasting univariate weather variable in Indonesian airport area and to explore the effect of intermediate weather variable related to accuracy prediction using single layer Long Short Memory Model (LSTM) model and multi layers LSTM model. The proposed forecasting model is an extension of LSTM model by adding intermediate variable signal into LSTM memory block. The premise is that two highly related patterns in input dataset will rectify the input patterns so make it easier for the model to learn and recognize the pattern from the training dataset. In an effort to achieve a robust model for learning and recognizing weather pattern, this research will also explore various architectures such as single layer LSTM and Multiple Layer LSTM (4 layers LSTM). The dataset is weather variable data collected by Weather Underground at Hang Nadim Indonesia airport. This research used visibility as predicted data and temperature, pressure, humidity, dew point as intermediates data. The best model of LSTM in this experiment is multiple layers LSTM and the best intermediate data is pressure variable. Using the pressure variable this model has gained the validation accuracy 0.8060 and RMSE 0.0775. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Long short-term memory

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