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The 2D Materials Roadmap

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arXiv 2025年

作者： Ren, Wencai Bøggild, Peter Redwing, Joan Marie Novoselov, Kostya Sun, Luzhao Qi, Yue Jia, Kaicheng Liu, Zhongfan Burton, Oliver Alexander-Webber, Jack Hofmann, Stephan Cao, Yang Long, Yu Yang, Quan-Hong Li, Dan Choi, Soo Ho Kim, Ki Kang Lee, Young Hee Li, Mian Huang, Qing Gogotsi, Yury Clark, Nicholas Carl, Amy Gorbachev, Roman Olsen, Thomas Rosen, Johanna Thygesen, Kristian Sommer Efetov, Dmitri K. Jessen, Bjarke S. Yankowitz, Matthew Barrier, Julien Kumar, Roshan Krishna Koppens, Frank H.L. Deng, Hui Li, Xiaoqin Dai, Siyuan Basov, D.N. Wang, Xinran Das, Saptarshi Duan, Xiangfeng Yu, Zhihao Borsch, Markus Ferrari, Andrea C. Huber, Rupert Kira, Mackillo Xia, Fengnian Wang, Xiao Wu, Zhong-Shuai Feng, Xinliang Simon, Patrice Cheng, Hui-Ming Liu, Bilu Xie, Yi Jin, Wanqin Nair, Rahul Raveendran Xu, Yan Zhang, Qing Katiyar, Ajit K. Ahn, Jong-Hyun Aharonovich, Igor Hersam, Mark C. Roche, Stephan Hua, Qilin Shen, Guozhen Ren, Tianling Zhang, Hao-Bin Koo, Chong Min Koratkar, Nikhil Pellegrini, Vittorio Young, Robert J. Qu, Bill Lemme, Max Pollard, Andrew J. Shenyang National Laboratory for Materials Science Institute of Metal Research Chinese Academy of Sciences 72 Wenhua Road Shenyang110016 China Technical University of Denmark Denmark The Pennsylvania State University United States University of Manchester United Kingdom Institute for Functional Intelligent Materials National University of Singapore Singapore Beijing Graphene Institute China University of Cambridge United Kingdom Department of Chemical Engineering The University of Melbourne Victoria Australia Nanoyang Group Tianjin Key Laboratory of Advanced Carbon and Electrochemical Energy Storage School of Chemical Engineering and Technology Tianjin University Tianjin300072 China The Hong Kong University of Science and Technology Hong Kong Center for Integrated Nanostructure Physics Institute for Basic Science Suwon16419 Korea Republic of Sungkyunkwan University Suwon16419 Korea Republic of Zhejiang Key Laboratory of Data-Driven High-Safety Energy Materials and Applications Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences China Drexel University United States Linköping University Sweden Ludwig-Maximilians-Universität München Germany University of Washington United States ICFO The Institute of Photonic Sciences Castelldefels Barcelona08860 Spain University of Michigan United States University of Texas Austin United States Auburn University United States Columbia University United States State Key Laboratory of Catalysis Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China University of California Los Angeles United States Suzhou Laboratory Suzhou China School of Integrated Circuit Science and Engineering Nanjing University of Posts and Telecommunications Nanjing210023 China Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States University of Regensburg Germany Department of Electrical and Computer Engineering Yale University New Have

Over the past two decades, 2D materials have rapidly evolved into a diverse and expanding family of material platforms. Many members of this materials class have demonstrated their potential to deliver transformative impact on fundamental research and technological applications across different fields. In this roadmap, we provide an overview of the key aspects of 2D material research and development, spanning synthesis, properties and commercial applications. We specifically present roadmaps for high impact 2D materials, including graphene and its derivatives, transition metal dichalcogenides, MXenes as well as their heterostructures and moiré systems. The discussions are organized into thematic sections covering emerging research areas (e.g., twisted electronics, moiré nano-optoelectronics, polaritronics, quantum photonics, and neuromorphic computing), breakthrough applications in key technologies (e.g., 2D transistors, energy storage, electrocatalysis, filtration and separation, thermal management, flexible electronics, sensing, electromagnetic interference shielding, and composites) and other important topics (computational discovery of novel materials, commercialization and standardization). This roadmap focuses on the current research landscape, future challenges and scientific and technological advances required to address, with the intent to provide useful references for promoting the development of 2D materials. © 2025, CC BY.

关键词： Integrated optoelectronics

data-driven model order reduction of linear switched systems

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arXiv 2017年

作者： Gosea, I.V. Petreczky, M. Antoulas, A.C. Data-Driven System Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany Umr Cnrs 9189 Cnrs Ecole Centrale de Lille Department of Electrical and Computer Engineering Rice University MS-366 6100 Main St HoustonTX77005 United States

The Loewner framework for model reduction is extended to the class of linear switched systems. One advantage of this framework is that it introduces a trade-off between accuracy and complexity. Moreover, through this procedure, one can derive state-space models directly from data which is related to the input-output behavior of the original system. Hence, another advantage of the framework is that it does not require the initial system matrices. More exactly, the data used in this framework consists in frequency domain samples of input-output mappings of the original system. The definition of generalized transfer functions for linear switched systems resembles the one for bilinear systems. A key role is played by the coupling matrices, which ensure the transition from one active mode to another. Copyright © 2017, The Authors. All rights reserved.

关键词： Frequency domain analysis

Balanced truncation for linear switched systems

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arXiv 2017年

作者： Gosea, I.V. Petreczky, M. Antoulas, A.C. Fiter, C. Data-Driven System Reduction and Identification Group Max Planck Institute for Dynamics of Complex Technical Systems Sandtorstrasse 1 Magdeburg39106 Germany UMR CNRS 9189 CNRS Ecole Centrale de Lille France Department of Electrical and Computer Engineering Rice University 6100 Main St MS-366 HoustonTX77005 United States CNRS CRIStAL UMR 9189 Université de Lille 1 Sciences et Technologies Villeneuve d'Ascq59651 France

We propose a model order reduction approach for balanced truncation of linear switched systems. Such systems switch among a finite number of linear subsystems or modes. We compute pairs of controllability and observability Gramians corresponding to each active discrete mode by solving systems of coupled Lyapunov equations. Depending on the type, each such Gramian corresponds to the energy associated to all possible switching scenarios that start or, respectively end, in a particular operational mode. In order to guarantee that hard to control and hard to observe states are simultaneously eliminated, we construct a transformed system, whose Gramians are equal and diagonal. Then, by truncation, directly construct reduced order models. One can show that these models preserve some properties of the original model, such as stability and that it is possible to obtain error bounds relating the observed output, the control input and the entries of the diagonal *** Codes 93A15, 93A30, 93B11, 93C05, 93C10 Copyright © 2017, The Authors. All rights reserved.

关键词： Lyapunov functions

Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird’s-Eye-View

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arXiv 2024年

作者： Das, Arindam Paul, Sudarshan Scholz, Niko Malviya, Akhilesh Kumar Sistu, Ganesh Bhattacharya, Ujjwal Eising, Ciarán The Department of Electronic and Computer Engineering University of Limerick LimerickV94 T9PX Ireland The Data Driven Computer Engineering Research Group University of Limerick LimerickV94 T9PX Ireland The Department of Driving Software and Systems Valeo India Chennai600130 India Valeo Driving Assistance Research Hummendorfer Str. 74 Kronach96317 Germany Valeo Vision Systems Dunmore Road Co. Galway TuamH54 Y276 Ireland Unit Indian Statistical Institute Kolkata700108 India

Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-view obstacle localization. However, the performance of such cameras can significantly deteriorate in low-light conditions, during nighttime, or when subjected to intense sun glare. Conversely, cost-effective sensors like ultrasonic sensors remain largely unaffected under these conditions. Therefore, we present, to our knowledge, the first end-to-end multimodal fusion model tailored for efficient obstacle perception in a bird’s-eye-view (BEV) perspective, utilizing fisheye cameras and ultrasonic sensors. Initially, ResNeXt-50 is employed as a set of unimodal encoders to extract features specific to each modality. Subsequently, the feature space associated with the visible spectrum undergoes transformation into BEV. The fusion of these two modalities is facilitated via concatenation. At the same time, the ultrasonic spectrum-based unimodal feature maps pass through content-aware dilated convolution, applied to mitigate the sensor misalignment between two sensors in the fused feature space. Finally, the fused features are utilized by a two-stage semantic occupancy decoder to generate grid-wise predictions for precise obstacle perception. We conduct a systematic investigation to determine the optimal strategy for multimodal fusion of both sensors. We provide insights into our dataset creation procedures, annotation guidelines, and perform a thorough data analysis to ensure adequate coverage of all scenarios. When applied to our dataset, the experimental results underscore the robustness and effectiveness of our proposed multimodal fusion approach. A short video highlighting an overview of this work along with its more qualitative results can be seen at https://***/JmSLBBL9Ruo. © 2024, CC BY.

关键词： Birds

data-driven (Bilinear) identification and reduction

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PAMM 2021年第S1期20卷

作者： Dimitrios S. Karachalios Ion Victor Gosea Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

Identifying dynamical systems from measured data is an important step towards accurate modeling and control. Model order reduction (MOR) constitutes a class of methods that can be used to replace large, complex models of dynamical processes with simpler, smaller models. The reduced-order models (ROMs) can be then used for further tasks such as control, design, and simulation. One typical approach for projection-based model reduction for both linear and nonlinear dynamical systems is by employing interpolation. Projection-based methods require access to the internal dynamics of the system which is not always available. The aim here is to compute ROMs without having access to the internal dynamics, by means of a realization independent method. The proposed methodology will fall into the broad category of data-driven approaches. The method under consideration, which will be referred to as the Loewner framework (LF), was originally introduced by the third author. Based on data, LF identifies state-space models in a direct way. In the original setup, the framework relies on compressing the full data set to extract dominant features and, at the same time, to eliminate the inherent redundancies. In the broader class of nonlinear control systems, the LF has been already extended to certain classes with a special structure such as quadratic or bilinear systems. The LF in combination with Volterra series-(VS) offers a non-intrusive approximation method that is capable of identifying bilinear models from input-output time-domain measurements. This method uses harmonic inputs which establish a natural way for data acquisition. The Fourier transform associates these measurements with the derived generalized frequency response functions GFRFs and the LF makes the connection with system theory. In the linear case, the LF associates the so-called S-parameters with the linear transfer function by interpolating in the frequency domain. The goal of the proposed method is to extend ide

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The Loewner framework for nonlinear identification and reduction of Hammerstein cascaded dynamical systems

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PAMM 2021年第1期20卷

We present an algorithm for data-driven identification and reduction of nonlinear cascaded systems with Hammerstein structure. The proposed algorithm relies on the Loewner framework (LF) which constitutes a non-intrusive algorithm for identification and reduction of dynamical systems based on interpolation. We address the following problem: the actuator (control input) enters a static nonlinear block. Then, this processed signal is used as an input for a linear time-invariant system (LTI). Additionally, it is considered that the orders of the linear transfer function and of the static nonlinearity are not a priori known.

关键词：

On computing reduced-order bilinear models from time-domain data

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PAMM 2021年第1期21卷

作者： Ion Victor Gosea Dimitrios S. Karachalios Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

We propose a procedure for identification of reduced-order bilinear models from time-domain data, i.e., sampled values of the control input and of the observed output. To accomplish this, we require two sets of data corresponding to the response of the system to two differently-scaled step functions, as control inputs. Then, the Markov parameters for two time-varying linear models are extracted, and from this information, we are able to infer a reduced-order bilinear model.

关键词：

A bilinear identification-modeling framework from time domain data

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PAMM 2019年第1期19卷

作者： Dimitrios S. Karachalios Ion Victor Gosea Athanasios C. Antoulas Max Planck Institute for Dynamics of Complex Technical Systems Data-Driven System Reduction and Identification (DRI) group Sandtorstraße 1 Magdeburg 39106 Rice University Houston Electrical and Computer Engineering Department 6100 Main St. Houston TX 77005 Baylor College of Medicine 1 Baylor Plaza Houston TX 77030

An ever-increasing need for improving the accuracy includes more involved and detailed features, thus inevitably leading to larger-scale dynamical systems [1]. To overcome this problem, efficient finite methods heavily rely on model reduction. One of the main approaches to model reduction of both linear and nonlinear systems is by means of interpolation. The Loewner framework is a direct data-driven method able to identify and reduce models derived directly from measurements. For measured data in the frequency domain, the Loewner framework is well established in linear case [2] while it has already extended to nonlinear [6]. On the other hand in the case of time domain data, the Loewner framework was already applied for approximating linear models [3]. In this study, an algorithm which uses time domain data for nonlinear (bilinear) system reduction and identification is presented.

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)