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Large tuning of the optical properties of nanoscale NdNiO3 via electron doping

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arXiv 2024年

作者： Jin, Yeonghoon Qu, Teng Kumar, Siddharth Kubzdela, Nicola Tsai, Cheng-Chia De Li, Tai Ramanathan, Shriram Yu, Nanfang Kats, Mikhail A. Department of Electrical and Computer Engineering University of Wisconsin-Madison Wisconsin53706 United States Department of Applied Physics and Applied Mathematics Columbia University New York10027 United States Department of Electrical and Computer Engineering Rutgers The State University of New Jersey New Jersey08854 United States CUNY Graduate Center Advanced Science Research Center New York11201 United States Department of Material Science and Engineering University of Wisconsin-Madison Wisconsin53706 United States

We synthesized crystalline films of neodymium nickel oxide (NdNiO3), a perovskite quantum material, switched the films from a metal phase (intrinsic) into an insulator phase (electron-doped) by field-driven lithium-ion intercalation, and characterized their structural and optical properties. Time-of-flight secondary-ion mass spectrometry (ToF-SIMS) showed that the intercalation process resulted in a gradient of the dopant concentration along the thickness direction of the films, turning the films into insulator-metal bilayers. We used variable-angle spectroscopic ellipsometry to measure the complex refractive indices of the metallic and insulating phases of NdNiO3. The insulator phase has a refractive index of n ~ 2 and low absorption in the visible and near infrared, and analysis of the complex refractive indices indicated that the band gap of the insulating phase is roughly 3-4 eV. Electrical control of the optical band gap, with corresponding large changes to the optical refractive indices, creates new opportunities for tunable optics. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy gap

On Metzler Positive Systems on Hypergraphs

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IEEE Transactions on Control of Network Systems 2025年

作者： Cui, Shaoxuan Zhang, Guofeng Jardon-Kojakhmetov, Hildeberto Cao, Ming Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands Department of Applied Mathematics The Hong Kong Polytechnic University Kowloon999077 Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen Guang Dong518057 China Engineering and Technology Institute Groningen University of Groningen Groningen9747 AG Netherlands

In graph-theoretical terms, an edge in a graph connects two vertices while a hyperedge of a hypergraph connects any more than one vertices. If the hypergraph's hyperedges further connect the same number of vertices, it is said to be uniform. In algebraic graph theory, a graph can be characterized by an adjacency matrix, and similarly, a uniform hypergraph can be characterized by an adjacency tensor. This similarity enables us to extend existing tools of matrix analysis for studying dynamical systems evolving on graphs to the study of a class of polynomial dynamical systems evolving on hypergraphs utilizing the properties of tensors. To be more precise, in this paper, we first extend the concept of a Metzler matrix to a Metzler tensor and then describe some useful properties of such tensors. Next, we focus on positive systems on hypergraphs associated with Metzler tensors. More importantly, we design control laws to stabilize the origin of this class of Metzler positive systems on hypergraphs. In the end, we apply our findings to two classic dynamical systems: a higher-order Lotka-Volterra population dynamics system and a higher-order SIS epidemic dynamic process. The corresponding novel stability results are accompanied by ample numerical examples. © 2025 IEEE.

关键词： Tensors

A mathematical model with numerical simulations for malaria transmission dynamics with differential susceptibility and partial immunity

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Healthcare Analytics 2023年 3卷 100165-100165页

作者： Tchoumi, S.Y. Rwezaura, H. Tchuenche, J.M. Department of Mathematics and Computer Sciences ENSAI – University of N'Gaoundéré N'Gaoundéré Cameroon Department of Mathematics and Applied Mathematics University of Pretoria South Africa Mathematics Department University of Dar es Salaam Dar es Salaam Tanzania School of Computational and Communication Sciences and Engineering Nelson Mandela African Institution of Science and Technology Arusha Tanzania School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa

Malaria is a deadly vector-borne infectious disease with high prevalence in the world's endemic tropical and subtropical regions. Differences in individuals’ disease susceptibility may lead to their differentiation of susceptibility to infection. We formulate a mathematical model for malaria transmission dynamics that accounts for the host's differential susceptibility, where partial immunity is acquired after infection. As customary, the explicit formula for the basic reproduction number is derived and used to determine the local stability of the model's equilibria. An analysis of a special case with two susceptible classes shows that the model could have two endemic equilibria when the disease threshold parameter is less than unity. Numerical simulations are provided for a differential susceptibility when individuals are re-infected seven times after the initial infection. Graphical representations show that the transient transmission dynamics of the infected components are indistinguishable when there is no inflow into the susceptible classes. When there is an inflow into the various susceptible classes, the graphs of the infected component of the model are fundamentally different, showing that individuals who have been infected multiple times tend to be less infected over time. Knowledge of the inflow rate and the infection reduction rate due to prior infection in each class could be key drivers to mitigate the burden of malaria in a community. © 2023 The Author(s)

关键词： Basic reproduction number Differential susceptibility Immunity Malaria Mathematical modeling Numerical simulation

Well-posedness analysis of the Cahn-Hilliard-Biot model

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arXiv 2023年

作者： Riethmüller, Cedric Storvik, Erlend Both, Jakub Wiktor Radu, Florin Adrian Institute of Applied Analysis and Numerical Simulation University of Stuttgart Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Center for Modeling of Coupled Subsurface Dynamics Department of Mathematics University of Bergen Norway

We investigate the well-posedness of the recently proposed Cahn-Hilliard-Biot model. The model is a three-way coupled PDE of elliptic-parabolic nature, with several nonlinearities and the fourth order term known to the Cahn-Hilliard system. We show existence of weak solutions to the variational form of the equations and uniqueness under certain conditions of the material parameters and secondary consolidation, adding regularizing effects. Existence is shown by discretizing in space and applying ODE-theory (the Peano-Cauchy theorem) to prove existence of the discrete system, followed by compactness arguments to retain solutions of the continuous system. In addition, the continuous dependence of solutions on the data is established, in particular implying uniqueness. Both results build strongly on the inherent gradient flow structure of the model. Copyright © 2023, The Authors. All rights reserved.

关键词： Variational techniques

Detecting 5G Signal Jammers with Autoencoders Based on Loose Observations

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Detecting 5G Signal Jammers with Autoencoders Based on Loose...

IEEE Globecom Workshops

作者： Matteo Varotto Stefan Valentin Stefano Tomasin Dep. of Information Engineering University of Padova Padova Italy Dep. of Computer Science Darmstadt University of Applied Sciences Darmstadt Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Padova Italy

Fifth-generation (5G) networks are prone to jamming attacks, which are particularly dangerous in mission-critical applications such as factory automation. In this paper, we present a simple method to detect jamming attacks with machine learning techniques operating on in-phase and quadrature (IQ) modulated signals. In particular, a convolutional autoencoder (CAE) learns the structure of the clean signal to distinguish it from jammed signals in real time. This approach requires only a loose synchronization to the OFDM symbol, while equalization and decoding are not necessary. Despite its simplicity, our technique has shown high detection rates in experiments on a 5G testbed.

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Theoretical Perspectives on Deep Learning Methods in Inverse Problems

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arXiv 2022年

作者： Scarlett, Jonathan Heckel, Reinhard Rodrigues, Miguel R.D. Hand, Paul Eldar, Yonina C. The Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore Singapore The Department of Electrical and Computer Engineering Technical University of Munich Germany The Department of Electronic and Electrical Engineering University College London United Kingdom The College of Science The Khoury College of Computer Sciences Northeastern University United States The Department of Computer Science and Applied Mathematics Weizmann Institute of Science Israel

In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resolution. While this line of works has predominantly been driven by practical algorithms and experiments, it has also given rise to a variety of intriguing theoretical problems. In this paper, we survey some of the prominent theoretical developments in this line of works, focusing in particular on generative priors, untrained neural network priors, and unfolding algorithms. In addition to summarizing existing results in these topics, we highlight several ongoing challenges and open problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Inverse problems

BLOCSIE - Benchmark for LOCalization in a Simulated Industrial Environment

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BLOCSIE - Benchmark for LOCalization in a Simulated Industri...

International Conference on Advanced Robotics (ICAR)

作者： Florian Spiess Daniel Bluemm Norbert Strobel Tobias Kaupp Samuel Kounev Faculty of Electrical Engineering Technical University of Applied Sciences Wuerzburg-Schweinfurt Schweinfurt Germany Institute of Medical Engineering Technical University of Applied Sciences Wuerzburg-Schweinfurt Schweinfurt Germany Institute Digital Engineering (IDEE) Technical University of Applied Sciences Wuerzburg-Schweinfurt Schweinfurt Germany Faculty of Mathematics and Computer Science Julius-Maximilians-University of Wuerzburg Wuerzburg Germany

Benchmarking of localization algorithms for autonomous mobile robots has become increasingly important as numerous algorithms have already been developed and continue to be proposed. To facilitate an objective comparison between various localization techniques based on different sensor types having specific sensor characteristics, e.g., LIDAR or color-range cameras (RGB-D), we propose the BLOCSIE (Benchmark for LOCalisation in a Simulated Industrial Environment) evaluation suite as a possible solution. It provides sensor simulation using realistic noise models, different scenarios, and the ability to use either predefined or self-defined paths for the creation of a dataset. Furthermore, it offers a toolkit for evaluating algorithm outputs, establishing a full end-to-end pipeline for benchmarking of mobile robot localization algorithms. In this paper, we provide a detailed description of BLOCSIE's capabilities. Based on an exemplary application, we highlight its functionalities as well as the importance of using a realistic noise model when analyzing the performance of localization algorithms.

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A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps

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arXiv 2023年

作者： Chang, Tyler H. Elias, Jakob R. Wild, Stefan M. Chaudhuri, Santanu Libera, Joseph A. Mathematics and Computer Science Division Argonne National Laboratory United States Applied Materials Division Argonne National Laboratory United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory United States Department of Civil Materials and Environmental Engineering University of Illinois Chicago United States

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

Multiphysics simulations of microstructure influence on hysteresis and eddy current losses of electrical steel

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arXiv 2025年

作者： Kühn, Patrick Yang, Yangyiwei Chen, Guanyu Foster, Shanelle N. Egger, Herbert Xu, Bai-Xiang Mechanics of Functional Materials Division Institute of Materials Science Technische Universität Darmstadt Darmstadt64287 Germany Department of Electrical and Computer Engineering Michigan State University MI48824 United States Johann Radon Institute for Computational and Applied Mathematics Linz Austria Institute for Numerical Mathematics Johannes Kepler University Linz Austria

Improving efficiency of electrical machines requires fundamental knowledge on the mechanisms behind magnetic and eddy current losses of the magnetic core materials, with Fe-Si alloy as a prototype. These losses are intrinsically influenced by the microstructure of the materials. This necessitates physics-based, microstructure-informed multiscale simulations. In the present paper, we utilised micromagnetic simulations and computational homogenization methods to calculate the effective hysteresis and effective conductivities of Fe-Si electrical steels. To demonstrate the methodology, binder-jet printed electrical steel material samples with different microstructure were investigated. The microstructure samples were digitized based on both the descriptor-based synthetic reconstruction and SEM-image-based digitization. More samples were generated with varying microstructure features such as grain size and grain boundary phases. The micromagnetic simulations were then performed to investigate the magnetic hysteresis and hysteresis loss. The eddy current loss was also evaluated by using the effective conductivity through computational homogenization. By performing parameter research on a series of synthetic microstructures, effects of average grain size and grain boundary (GB) phase thickness on the hysteresis loss and eddy current loss were unveiled. An average grain size around 120 µm has the lowest hysteresis loss, although the eddy current loss increases with the grain size. Increasing GB-phase thickness helps reduce both losses. Results indicate the potential to decrease loss of magnetic core materials by microstructure optimization. © 2025, CC BY.

关键词： Silicon alloys