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A generative deep learning framework for airfoil flow field prediction with sparse data

Chinese Journal of Aeronautics 2022年第1期35卷 470-484页

作者： Haizhou WU Xuejun LIU Wei AN Hongqiang LYU MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Aerodynamic Noise Control Mianyang 621000China State Key Laboratory of Aerodynamics Mianyang 621000China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210023China College of Aerospace Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Deep learning has been probed for the airfoil performance prediction in recent *** with the expensive CFD simulations and wind tunnel experiments,deep learning models can be leveraged to somewhat mitigate such expenses with proper ***,effective training of the data-driven models in deep learning severely hinges on the data in diversity and *** this paper,we present a novel data augmented Generative Adversarial Network(GAN),daGAN,for rapid and accurate flow filed prediction,allowing the adaption to the task with sparse *** presented approach consists of two modules,pre-training module and fine-tuning *** pre-training module utilizes a conditional GAN(cGAN)to preliminarily estimate the distribution of the training *** the fine-tuning module,we propose a novel adversarial architecture with two generators one of which fulfils a promising data augmentation operation,so that the complement data is adequately incorporated to boost the generalization of the *** use numerical simulation data to verify the generalization of daGAN on airfoils and flow conditions with sparse training *** results show that daGAN is a promising tool for rapid and accurate evaluation of detailed flow field without the requirement for big training data.

关键词： CFD Flow field Generative adversarial networks(GANs) Sparse data Supercritical airfoil

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Semi-blind compressed sensing via adaptive dictionary learning and one-pass online extension

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Science China(Information Sciences) 2021年第9期64卷 235-237页

作者： Di MA Songcan CHEN College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics

Dear editor,Compressed sensing(CS) [1], as an efficient data acquisition paradigm, has attracted much attentions since it came up. The fundamental principle of CS is that a signal, which is sparse under some sparsity basis, can be efficiently acquired and accurately recovered via far fewer measurements than the traditional Shannon-Nyquist sampling. Since the number of measurements is much smaller than the number of signal coefficients,

关键词： Information Systems and Communication Service

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Minimum Error Entropy High-Order Extended Kalman Filter

Minimum Error Entropy High-Order Extended Kalman Filter

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2023 International Conference on Cyber-Physical Social intelligence, ICCSI 2023

作者： Cheng, Zhi Chen, Xiaofeng Li, Hua Li, Weikai Lin, Dongyuan Chongqing Jiaotong University Department of Mathematics Chongqing China Miit Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Southwest University College of Electronic and Information Engineering Chongqing China

ISBN: (纸本)9798350312492

The conventional extended Kalman filter(EKF) is proposed under the criterion of minimum mean square error(MMSE) and omits high-order information. This leads to poor estimation of the EKF in non-Gaussian and strongly nonlinear systems. In this paper, for a class of nonlinear non-Gaussian systems in polynomial form, Pseudo-linearizing the system while preserving the high-order information, a new filter is proposed based on the minimum error entropy(MEE) criterion called the minimum error entropy high-order extended Kalman filter (MEE-HEKF). In the MEE-HEKF algorithm, the state estimation problem is transformed into a recursive solution problem in the form of a Kalman filter by a fixed-point iterative approach based on the MEE cost function. The proposed algorithm has better robustness under strong nonlinearity and non-Gaussian noise. Finally, the effectiveness of the new algorithm is illustrated by an example. © 2023 IEEE.

关键词： Extended Kalman filters

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A Tailored Physics-informed Neural Network Method for Solving Singularly Perturbed Differential Equations 5

A Tailored Physics-informed Neural Network Method for Solvin...

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5th International Conference on Algorithms, Computing and Artificial intelligence, ACAI 2022

作者： Pang, Yiwen Li, Ye Huang, Sheng-Jun College of Computer Science and Technology/Artificial Intelligence Nanjing University of Aeronautics and Astronautics China College of Computer Science and Technology/Artificial Intelligence Nanjing University of Aeronautics and Astronautics China and MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China

ISBN: (纸本)9781450398343

Physics-informed neural networks (PINNs) have recently been demonstrated to be effective for the numerical solution of differential equations, with the advantage of small real labelled data needed. However, the performance of PINN greatly depends on the differential equation. The solution of singularly perturbed differential equations (SPDEs) usually contains a boundary layer, which makes it difficult for PINN to approximate the solution of SPDEs. In this paper, we analyse the reasons for the failure of PINN in solving SPDE and provide a feasible solution by adding prior knowledge of the boundary layer to the neural network. The new method is called the tailored physics-informed neural network (TPINN) since the network is tailored to some particular properties of the problem. Numerical experiments show that our method can effectively improve both the training speed and accuracy of neural networks. © 2022 ACM.

关键词： Differential equations

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machine Learning for Brain Imaging Genomics Methods:A Review

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machine intelligence Research 2023年第1期20卷 57-78页

作者： Mei-Ling Wang Wei Shao Xiao-Ke Hao Dao-Qiang Zhang College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Pattern Analysis and Machine Intelligence Ministry of Industry and Information TechnologyNanjing 211106China School of Artificial Intelligence Hebei University of TechnologyTianjin 300401China

In the past decade,multimodal neuroimaging and genomic techniques have been increasingly *** an interdiscip-linary topic,brain imaging genomics is devoted to evaluating and characterizing genetic variants in individuals that influence phenotyp-ic measures derived from structural and functional brain *** technique is capable of revealing the complex mechanisms by macroscopic intermediates from the genetic level to cognition and psychiatric disorders in *** is well known that machine learn-ing is a powerful tool in the data-driven association studies,which can fully utilize priori knowledge(intercorrelated structure informa-tion among imaging and genetic data)for association *** addition,the association study is able to find the association between risk genes and brain structure or function so that a better mechanistic understanding of behaviors or disordered brain functions is *** this paper,the related background and fundamental work in imaging genomics are first ***,we show the univari-ate learning approaches for association analysis,summarize the main idea and modelling in genetic-imaging association studies based on multivariate machine learning,and present methods for joint association analysis and outcome ***,this paper discusses some prospects for future work.

关键词： Brain imaging genomics machine learning multivariate analysis association analysis outcome prediction

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Characteristic AI Agents via Large Language Models 30

Characteristic AI Agents via Large Language Models

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Xi Dai, Hongliang Gao, Shen Li, Piji College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China School of Computer Science and Technology Shandong University China

ISBN: (纸本)9782493814104

The advancement of Large Language Models (LLMs) has led to significant enhancements in the performance of chatbot systems. Many researchers have dedicated their efforts to the development of bringing characteristics to chatbots. While there have been commercial products for developing role-driven chatbots using LLMs, it is worth noting that academic research in this area remains relatively scarce. Our research focuses on investigating the performance of LLMs in constructing Characteristic AI Agents by simulating real-life individuals across different settings. Current investigations have primarily focused on act on roles with simple profiles. In response to this research gap, we create a benchmark for the characteristic AI agents task, including dataset, techniques, and evaluation metrics. A dataset called "Character100" is built for this benchmark, comprising the most-visited people on Wikipedia for language models to role-play. With the constructed dataset, we conduct comprehensive assessment of LLMs across various settings. In addition, we devise a set of automatic metrics for quantitative performance evaluation. The experimental results underscore the potential directions for further improvement in the capabilities of LLMs in constructing characteristic AI agents. The benchmark is available at https://***/nuaa-nlp/Character100. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Deep Self-Reconstruction Sparse Canonical Correlation analysis For Brain Imaging Genetics

Deep Self-Reconstruction Sparse Canonical Correlation Analys...

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IEEE International Symposium on Biomedical Imaging

作者： Meiling Wang Wei Shao Shuo Huang Daoqiang Zhang MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics

Brain imaging genetics is an emerging research field to explore the underlying genetic architecture of brain structure and function measured by different imaging modalities. As a bi-multivariate technique for brain imaging genetics, sparse canonical correlation analysis (SCCA) has the good ability to identify complex multi-SNP-multi-QT associations. However, for most current brain imaging genetics with SCCA, there exist three main challenges for calculating accurate bi-multivariate relationships and selecting relevant features, i.e., nonlinearity, high-dimensionality (across all 4005 network edges between 90 brain regions), and a small number of subjects. We propose a novel deep self-reconstruction sparse canonical correlation analysis (DS-SCCA) for solving mentioned challenges in brain imaging genetics problems. Specifically, we employ deep network, i.e., multiple stacked layers of nonlinear transformation, as the kernel function, and learn the self-reconstruction matrix to reconstruct the original data at the top layer of the network. The parameters of our model are iteratively learned using parametric approach, augmented Lagrange method, and stochastic gradient descent for optimization. Experimental results on ADNI dataset are given to demonstrate that our method produces improved cross-validation performances and biologically meaningful results.

关键词： Brain Correlation Image edge detection Imaging Stochastic processes Genetics Sparse matrices

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Forgetting, Ignorance or Myopia: Revisiting key Challenges in Online Continual Learning 38

Forgetting, Ignorance or Myopia: Revisiting Key Challenges i...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wang, Xinrui Geng, Chuanxing Wan, Wenhai Li, Shao-Yuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China School of Computer Science and Technology Huazhong University of Science and Technology China

Online continual learning (OCL) requires the models to learn from constant, endless streams of data. While significant efforts have been made in this field, most were focused on mitigating the catastrophic forgetting issue to achieve better classification ability, at the cost of a much heavier training workload. They overlooked that in real-world scenarios, e.g., in high-speed data stream environments, data do not pause to accommodate slow models. In this paper, we emphasize that model throughput- defined as the maximum number of training samples that a model can process within a unit of time - is equally important. It directly limits how much data a model can utilize and presents a challenging dilemma for current methods. With this understanding, we revisit key challenges in OCL from both empirical and theoretical perspectives, highlighting two critical issues beyond the well-documented catastrophic forgetting: (i) Model's ignorance: the single-pass nature of OCL challenges models to learn effective features within constrained training time and storage capacity, leading to a trade-off between effective learning and model throughput;(ii) Model's myopia: the local learning nature of OCL on the current task leads the model to adopt overly simplified, task-specific features and excessively sparse classifier, resulting in the gap between the optimal solution for the current task and the global objective. To tackle these issues, we propose the Non-sparse Classifier Evolution framework (NsCE) to facilitate effective global discriminative feature learning with minimal time cost. NsCE integrates non-sparse maximum separation regularization and targeted experience replay techniques with the help of pre-trained models, enabling rapid acquisition of new globally discriminative features. Extensive experiments demonstrate the substantial improvements of our framework in performance, throughput and real-world practicality. © 2024 Neural information processing systems foundation.

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All-around Neural Collapse for Imbalanced Classification

arXiv

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arXiv 2024年

作者： Zhang, Enhao Li, Chaohua Geng, Chuanxing Chen, Songcan MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China Nanjing211106 China

Neural Collapse (NC) presents an elegant geometric structure that enables individual activations (features), class means and classifier (weights) vectors to reach optimal interclass separability during the terminal phase of training on a balanced dataset. Once shifted to imbalanced classification, such an optimal structure of NC can be readily destroyed by the notorious minority collapse, where the classifier vectors corresponding to the minority classes are squeezed. In response, existing works endeavor to recover NC typically by optimizing classifiers. However, we discover that this squeezing phenomenon is not only confined to classifier vectors but also occurs with class means. Consequently, reconstructing NC solely at the classifier aspect may be futile, as the feature means remain compressed, leading to the violation of inherent self-duality in NC (i.e., class means and classifier vectors converge mutually) and incidentally, resulting in an unsatisfactory collapse of individual activations towards the corresponding class means. To shake off these dilemmas, we present a unified All-around Neural Collapse framework (AllNC), aiming to comprehensively restore NC across multiple aspects including individual activations, class means and classifier vectors. We thoroughly analyze its effectiveness and verify on multiple benchmark datasets that it achieves state-of-the-art in both balanced and imbalanced settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Exploiting Saliency in Attention Based Convolutional Neural Network for Classification of Vertical Root Fractures 25th

Exploiting Saliency in Attention Based Convolutional Neural ...

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25th International Conference on pattern Recognition Workshops, ICPR 2020

作者： Xu, Zhenxing Wan, Peng Aihemaiti, Gulibire Zhang, Daoqiang MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Affiliated Stomatology Hospital of Medical School Nanjing University Nanjing China

ISBN: (纸本)9783030687625

Cone-beam computed tomography (CBCT) is widely used in clinical diagnosis of vertical root fractures (VRFs) which presents as crack on the teeth. However, manually checking the VRFs from a larger number of CBCT images is time-consuming and error-prone. Although the Convolutional Neural Networks (CNN) have achieved unprecedented progress in natural image recognition, end-to-end CNN is unsuitable to identify VRFs due to crack appears to be multi-scales and their complex relationships with surroundings tissues. We proposed a novel Feature Pyramids Attention Convolutional Neural Network (FPA-CNN), which incorporates saliency mask and multi-scale feature to boost the classification performance. Saliency map is viewed as spatial probability map where a person might look first to make a discriminative conclusion. Therefore it plays a role of high-level hint to guide the network focusing on the discriminative region. Experimental results demonstrate that our proposed FPA-CNN overcomes the challenge arised from multi-scale crack and complex contextual relationships. © 2021, Springer Nature Switzerland AG.

关键词： Cracks

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