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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4443-4457页

作者： Khan, Khurram ur Rehman, Atiq Khan, Adnan Naqvi, Syed Rameez Belhaouari, Samir Brahim Bermak, Amine Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Haripur22620 Pakistan Hamad Bin Khalifa University Division of Information and Computing Technology Doha34110 Qatar Comsats University Islamabad Wah47040 Pakistan

This work proposes a novel split and kernel-merge clustering (S-KMC), a nonparametric clustering algorithm that combines the strengths of hierarchical clustering, partitional clustering, and density-based clustering. It consists of two main phases: splitting and merging. In the splitting phase, a ranking-based operator is used to divide the data into optimal subclusters. In the merging phase, a kernel function estimates the density of these subclusters after projecting them onto a straight line passing through their centers, facilitating the merging operation. S-KMC is fully nonparametric, eliminating the need for prior information about the data. It effectively handles 1) shape diversity, 2) density variability, 3) high dimensionality, 4) outliers, and 5) missing values. The algorithm offers easily tunable hyperparameters, enhancing its applicability to complex problems and robustness against data anomalies. Experimental analysis on 21 benchmark datasets demonstrates the improved performance of S-KMC in terms of cluster accuracy, handling high-dimensional data, and managing data anomalies and outliers. Comprehensive comparisons with state-of-the-art techniques further validate the superior or comparable performance of the proposed S-KMC algorithm. © 2020 IEEE.

关键词： Clustering algorithms

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Explainable machine learning in materials science

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npj Computational Materials 2022年第1期8卷 1951-1969页

作者： Xiaoting Zhong Brian Gallagher Shusen Liu Bhavya Kailkhura Anna Hiszpanski T.Yong-Jin Han Materials Science Division Lawrence Livermore National LaboratoryLivermoreCAUSA Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCAUSA

Machine learning models are increasingly used in materials studies because of their exceptional ***,the most accurate machine learning models are usually difficult to *** to this problem lie in explainable artificial intelligence(XAI),an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks(DNNs).This article attempts to provide an entry point to XAI for materials *** are defined to clarify what explain means in the context of materials *** works are reviewed to show how XAI helps materials science *** and opportunities are also discussed.

关键词： explain learning plain

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Prediction of COVID-19 from Lung Scans Using Deep Learning 6

Prediction of COVID-19 from Lung Scans Using Deep Learning

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6th International Conference on Contemporary computing and Informatics, IC3I 2023

作者： Pramanik, Atreyi Sinha, Aashna Aluvala, Srinivas School of Applied and Life Sciences Division of Research and Innovation Uttaranchal University Dehradun India School of Computing Sciences S R University Telangana Hyderabad India

ISBN: (纸本)9798350304480

The COVID-19 pandemic has placed enormous demands on the world's healthcare systems, needing creative methods for the early and precise diagnosis of sickness. In this review study, we investigate the application of deep learning methods for COVID-19 prediction from lung scans. Deep learning methods, in particular convolutional neural networks (CNNs), have demonstrated remarkable promise in separating COVID-19 infections from other respiratory diseases by utilizing the power of artificial intelligence. We analyze the various approaches and datasets used in deep learning-based COVID-19 prediction studies through a thorough examination of the available literature. The paper examines the difficulties with deep learning models'interpretability as well as potential biases that could appear during training and inference. We also investigate the use of pre-training and transfer learning methods to enhance model convergence and overcome data scarcity issues. The importance of early detection and prognostic skills made possible by deep learning models is also highlighted in this research, underlining their potential to direct clinical decision-making and resource allocation. We look at the consequences of model robustness in controlling the ever-evolving pandemic as well as the adaptability of these models to new COVID-19 variants. Overall, this review paper summarizes the state of deep learning-based COVID-19 prediction from lung scans and discusses its accomplishments, difficulties, and potential future approaches. We want to contribute to the development of AI-driven diagnoses and support healthcare professionals in making well-informed decisions to successfully tackle the COVID-19 epidemic by critically reviewing the available studies. © 2023 IEEE.

关键词： convolutional neural networks COVID-19 CT scans deep learning prediction prognosis X-ray lung images

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Optimizing Atomic Layer Deposition Using a Hybrid of Machine Learning Methods 9

Optimizing Atomic Layer Deposition Using a Hybrid of Machine...

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9th International Conference on Engineering and Emerging Technology, ICEET 2023

作者： Dibenedetto, Anthony Abuomar, Osama Yanguas-Gil, Angel Elam, Jeffrey W. Lewis University Department of Engineering Computing and Mathematical Sciences RomeovilleIL United States Argonne National Laboratory Applied Materials Division LemontIL United States

ISBN: (纸本)9798350316926

Atomic layer deposition (ALD) is a crucial technique in semiconductor miniaturization and high-precision applications. The quality of ALD processes directly affects the properties of the resulting thin films, leading to extensive evaluations for new ALD procedures. This study uses machine learning to quickly assess ALD process quality, with a focus on predicting the standard deviations of film thickness as an indicator of quality. Using a synthetic dataset simulating non-ideal ALD processes, we evaluated the performance of Random Forest Classifier (RFC), Support Vector Machines (SVMs), and K-Nearest Neighbor (KNN). We also introduced artificial neural network (ANN) and convolutional neural network (CNN) models for predicting standard deviations of film thickness from ALD trials. Our ANN and CNN models showed promising results, positioning them as reliable tools for predicting ALD process quality. © 2023 IEEE.

关键词： Atomic layer deposition

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Efficient and interpretable graph network representation for angle-dependent properties applied to optical spectroscopy

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npj Computational Materials 2022年第1期8卷 1434-1442页

作者： Tim Hsu Tuan Anh Pham Nathan Keilbart Stephen Weitzner James Chapman Penghao Xiao S.Roger Qiu Xiao Chen Brandon C.Wood Center for Applied Scientific Computing Lawrence Livermore National LaboratoryLivermoreCAUSA Materials Science Division Lawrence Livermore National LaboratoryLivermoreCAUSA Department of Physics and Atmospheric Science Dalhousie UniversityHalifaxNSCanada

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and ***,conventional encoding does not include angular information,which is critical for describing atomic arrangements in disordered *** this work,we extend the recently proposed ALIGNN(Atomistic Line Graph Neural Network)encoding,which incorporates bond angles,to also include dihedral angles(ALIGNN-d).This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic ***-d is applied to predict the infrared optical response of dynamically disordered Cu(II)aqua complexes,leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural *** and dihedral angles are found to be critical contributors to the fine structure of the absorption response,with distortions that represent transitions between more common geometries exhibiting the strongest absorption *** directions for further development of ALIGNN-d are discussed.

关键词： representation disordered absorption

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RKDG Methods with Multi-resolution WENO Limiters for Solving Steady-State Problems on Triangular Meshes

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Communications on applied Mathematics and Computation 2024年第3期6卷 1575-1599页

作者： Jun Zhu Chi-Wang Shu Jianxian Qiu State Key Laboratory of Mechanics and Control of Mechanical Structures and Key Laboratory of Mathematical Modelling and High Performance Computing of Air Vehicles(NUAA) MIITNanjing University of Aeronautics and AstronauticsNanjing 210016JiangsuChina Division of Applied Mathematics Brown UniversityProvidenceRI 02912USA School of Mathematical Sciences and Fujian Provincial Key Laboratory of Mathematical Modeling and High-Performance Scientific ComputingXiamen UniversityXiamen 361005FujianChina

In this paper, we design high-order Runge-Kutta discontinuous Galerkin (RKDG) methods with multi-resolution weighted essentially non-oscillatory (multi-resolution WENO) limiters to compute compressible steady-state problems on triangular meshes. A troubled cell indicator extended from structured meshes to unstructured meshes is constructed to identify triangular cells in which the application of the limiting procedures is required. In such troubled cells, the multi-resolution WENO limiting methods are used to the hierarchical L^(2) projection polynomial sequence of the DG solution. Through using the RKDG methods with multi-resolution WENO limiters, the optimal high-order accuracy can be gradually reduced to first-order in the triangular troubled cells, so that the shock wave oscillations can be well suppressed. In steady-state simulations on triangular meshes, the numerical residual converges to near machine zero. The proposed spatial reconstruction methods enhance the robustness of classical DG methods on triangular meshes. The good results of these RKDG methods with multi-resolution WENO limiters are verified by a series of two-dimensional steady-state problems.

关键词： RKDG method Steady-state problem Multi-resolution WENO limiter Triangular mesh Machine zero

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Reduced Order Modeling for First Order Hyperbolic Systems with Application to Multiparameter Acoustic Waveform Inversion

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SIAM Journal on Imaging Sciences 2025年第2期18卷 851-880页

作者： Borcea, Liliana Garnier, Josselin Mamonov, Alexander V. Zimmerling, Jorn Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States CMAP CNRS Ecole Polytechnique Institut Polytechnique de Paris Palaiseau91120 France Department of Mathematics University of Houston HoustonTX77204-3008 United States Department of Information Technology Division of Scientific Computing Uppsala Universitet Uppsala75105 Sweden

Waveform inversion seeks to estimate an inaccessible heterogeneous medium from data gathered by sensors that emit probing signals and measure the generated waves. It is an inverse problem for a second order wave equation or a first order hyperbolic system, with the sensor excitation modeled as a forcing term and the heterogeneous medium described by unknown, spatially variable coefficients. The traditional ``full waveform inversion"" (FWI) formulation estimates the unknown coefficients via minimization of the nonlinear, least squares data fitting objective function. For typical band-limited and high frequency data, this objective function has spurious local minima near and far from the true coefficients. Thus, FWI implemented with gradient based optimization algorithms may fail, even for good initial guesses. Recently, it was shown that it is possible to obtain a better behaved objective function for wave speed estimation, using data driven reduced order models (ROMs) that capture the propagation of pressure waves, governed by the classic second order wave equation. Here we introduce ROMs for vectorial waves, satisfying a general first order hyperbolic system. They are defined via Galerkin projection on the space spanned by the wave snapshots, evaluated on a uniform time grid with appropriately chosen time step. Our ROMs are data driven: They are computed in an efficient and noniterative manner, from the sensor measurements, without knowledge of the medium and the snapshots. The ROM computation applies to any linear waves in lossless and nondispersive media. For the inverse problem we focus attention on acoustic waves in a medium with unknown variable wave speed and density. We show that these can be determined via minimization of an objective function that uses a ROM based approximation of the vectorial wave field inside the inaccessible medium. We assess the performance of our inversion approach with numerical simulations and compare the results to those given by

关键词： Inverse problems

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EXTENDED GALERKIN NEURAL NETWORK APPROXIMATION OF SINGULAR VARIATIONAL PROBLEMS WITH ERROR CONTROL

arXiv

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arXiv 2024年

作者： Ainsworth, Mark Dong, Justin Division of Applied Mathematics Brown University United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

We present extended Galerkin neural networks (xGNN), a variational framework for approximating general boundary value problems (BVPs) with error control. The main contributions of this work are (1) a rigorous theory guiding the construction of new weighted least squares variational formulations suitable for use in neural network approximation of general BVPs (2) an "extended" feedforward network architecture which incorporates and is even capable of learning singular solution structures, thus greatly improving approximability of singular solutions. Numerical results are presented for several problems including steady Stokes flow around re-entrant corners and in convex corners with Moffatt eddies in order to demonstrate efficacy of the method. © 2024, CC BY-NC-SA.

关键词： Partial differential equations

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A Novel Feature Extraction Technique for ECG Arrhythmia Classification Using ML

A Novel Feature Extraction Technique for ECG Arrhythmia Clas...

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2023 IEEE International Conference on Dependable, Autonomic and Secure computing, 2023 International Conference on Pervasive Intelligence and computing, 2023 International Conference on Cloud and Big Data computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Rahman, Mohammad Mominur Islam, Ashhadul Charni, Skander Bensmail, Halima Hilbel, Thomas Belhaouari, Samir Brahim College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar College of Engineering Qatar University Doha Qatar Doha Qatar Westphalian University of Applied Sciences Department of Electrical Engineering and Applied Sciences Gelsenkirchen Germany

ISBN: (纸本)9798350304602

Feature extraction is the process of transforming raw data into features that are more relevant for machine learning algorithms. The goal of feature extraction is to find a set of features that can be used to accurately predict the target variable. The specific features that are extracted will depend on the specific application. For example, features that are extracted for the purpose of diagnosing arrhythmias will be different from the features that are extracted for the purpose of assessing myocardial infarction. A generalized new algorithm for feature extraction could be helpful for all complex feature extraction data sets. In this paper, we propose a random selection process to generate the required number of new features with the help of existing specific features of the electrocardiogram (ECG) signal. We have named this novel feature extraction method the Random Feature Explorer (RFE). The proposed method was tested and evaluated using Physio Net's MIT-BIH datasets. The results indicate that the suggested method achieved an accuracy of 99.79% in arrhythmia classification. We have made the source code for our proposed method available on GitHub for open access and reproducibility. The code can be accessed at https://***/3NnrH4A © 2023 IEEE.

关键词： Electrocardiograms

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