scholarly journals Sparse identification of nonlinear dynamics with low-dimensionalized flow representations

2021 ◽  
Vol 926 ◽  
Author(s):  
Kai Fukami ◽  
Takaaki Murata ◽  
Kai Zhang ◽  
Koji Fukagata
Keyword(s):  
Nonlinear Dynamics ◽  
Governing Equation ◽  
Transient Flow ◽  
Stable Solution ◽  
Least Square ◽  
High Dimensional ◽  
Cylinder Wake ◽  
Latent Vector ◽  
Wide Range ◽  
Low Dimensional

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized complex flow phenomena. We first apply the SINDy with two regression methods, the thresholded least square algorithm and the adaptive least absolute shrinkage and selection operator which show reasonable ability with a wide range of sparsity constant in our preliminary tests, to a two-dimensional single cylinder wake at $Re_D=100$ , its transient process and a wake of two-parallel cylinders, as examples of high-dimensional fluid data. To handle these high-dimensional data with SINDy whose library matrix is suitable for low-dimensional variable combinations, a convolutional neural network-based autoencoder (CNN-AE) is utilized. The CNN-AE is employed to map a high-dimensional dynamics into a low-dimensional latent space. The SINDy then seeks a governing equation of the mapped low-dimensional latent vector. Temporal evolution of high-dimensional dynamics can be provided by combining the predicted latent vector by SINDy with the CNN decoder which can remap the low-dimensional latent vector to the original dimension. The SINDy can provide a stable solution as the governing equation of the latent dynamics and the CNN-SINDy-based modelling can reproduce high-dimensional flow fields successfully, although more terms are required to represent the transient flow and the two-parallel cylinder wake than the periodic shedding. A nine-equation turbulent shear flow model is finally considered to examine the applicability of SINDy to turbulence, although without using CNN-AE. The present results suggest that the proposed scheme with an appropriate parameter choice enables us to analyse high-dimensional nonlinear dynamics with interpretable low-dimensional manifolds.

Processing Top-N Queries Based on p-Norm Distances

2014 ◽  
Vol 490-491 ◽  
pp. 1293-1297
Author(s):  
Liang Zhu ◽  
Fei Fei Liu ◽  
Wu Chen ◽  
Qing Ma
Keyword(s):  
Fundamental Principle ◽  
High Dimensional ◽  
Query Point ◽  
Ranking Functions ◽  
Important Method ◽  
Wide Range ◽  
Effectiveness And Efficiency ◽  
Data Objects ◽  
Low Dimensional ◽  
Ranked List

Top-Nqueries are employed in a wide range of applications to obtain a ranked list of data objects that have the highest aggregate scores over certain attributes. The threshold algorithm (TA) is an important method in many scenarios. However, TA is effective only when the ranking function is monotone and the query point is fixed. In the paper, we propose an approach that alleviates the limitations of TA-like methods for processing top-Nqueries. Based onp-norm distances as ranking functions, our methods utilize the fundamental principle of Functional Analysis so that the candidate tuples of top-Nquery with ap-norm distance can be obtained by the Maximum distance. We conduct extensive experiments to prove the effectiveness and efficiency of our method for both low-dimensional (2, 3 and 4) and high-dimensional (25,50 and 104) data.

scholarly journals EVALUATING UNIFORM MANIFOLD APPROXIMATION AND PROJECTION FOR DIMENSION REDUCTION AND VISUALIZATION OF POLINSAR FEATURES

2021 ◽  
Vol V-1-2021 ◽  
pp. 39-46
Author(s):  
S. Schmitz ◽  
U. Weidner ◽  
H. Hammer ◽  
A. Thiele
Keyword(s):  
Dimension Reduction ◽  
Visual Analysis ◽  
Principal Component ◽  
Feature Space ◽  
Decomposition Methods ◽  
High Dimensional ◽  
Feature Representations ◽  
Wide Range ◽  
Nonlinear Dimension ◽  
Low Dimensional

Abstract. In this paper, the nonlinear dimension reduction algorithm Uniform Manifold Approximation and Projection (UMAP) is investigated to visualize information contained in high dimensional feature representations of Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data. Based on polarimetric parameters, target decomposition methods and interferometric coherences a wide range of features is extracted that spans the high dimensional feature space. UMAP is applied to determine a representation of the data in 2D and 3D euclidean space, preserving local and global structures of the data and still suited for classification. The performance of UMAP in terms of generating expressive visualizations is evaluated on PolInSAR data acquired by the F-SAR sensor and compared to that of Principal Component Analysis (PCA), Laplacian Eigenmaps (LE) and t-distributed Stochastic Neighbor embedding (t-SNE). For this purpose, a visual analysis of 2D embeddings is performed. In addition, a quantitative analysis is provided for evaluating the preservation of information in low dimensional representations with respect to separability of different land cover classes. The results show that UMAP exceeds the capability of PCA and LE in these regards and is competitive with t-SNE.

scholarly journals General Collaborative Filtering for Web Service QoS Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Wenming Ma ◽  
Rongjie Shan ◽  
Mingming Qi
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Matrix Factorization ◽  
Experimental Studies ◽  
Low Rank ◽  
Qos Prediction ◽  
Latent Vector ◽  
Wide Range ◽  
Regularization Techniques ◽  
Low Dimensional

To avoid the expensive and time-consuming evaluation, collaborative filtering (CF) methods have been widely studied for web service QoS prediction in recent years. Among the various CF techniques, matrix factorization is the most popular one. Much effort has been devoted to improving matrix factorization collaborative filtering. The key idea of matrix factorization is that it assumes the rating matrix is low rank and projects users and services into a shared low-dimensional latent space, making a prediction by using the dot product of a user latent vector and a service latent vector. Unfortunately, unlike the recommender systems, QoS usually takes continuous values with very wide range, and the low rank assumption might incur high bias. Furthermore, when the QoS matrix is extremely sparse, the low rank assumption also incurs high variance. To reduce the bias, we must use more complex assumptions. To reduce the variance, we can adopt complex regularization techniques. In this paper, we proposed a neural network based framework, named GCF (general collaborative filtering), with the dropout regularization, to model the user-service interactions. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 users on 5825 web services. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.

Geometry and Physics: Volume II

2018 ◽  
Keyword(s):  
Moduli Spaces ◽  
Mirror Symmetry ◽  
Geometric Analysis ◽  
Mathematical Physics ◽  
Poisson Geometry ◽  
Special Holonomy ◽  
Low Dimensional Topology ◽  
Generalized Complex Structures ◽  
Wide Range ◽  
Low Dimensional

These volumes contain the proceedings of the conference held at Aarhus, Oxford and Madrid in September 2016 to mark the seventieth birthday of Nigel Hitchin, one of the world’s foremost geometers and Savilian Professor of Geometry at Oxford. The proceedings contain twenty-nine articles, including three by Fields medallists (Donaldson, Mori and Yau). The articles cover a wide range of topics in geometry and mathematical physics, including the following: Riemannian geometry, geometric analysis, special holonomy, integrable systems, dynamical systems, generalized complex structures, symplectic and Poisson geometry, low-dimensional topology, algebraic geometry, moduli spaces, Higgs bundles, geometric Langlands programme, mirror symmetry and string theory. These volumes will be of interest to researchers and graduate students both in geometry and mathematical physics.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 743
Author(s):  
Xi Liu ◽  
Shuhang Chen ◽  
Xiang Shen ◽  
Xiang Zhang ◽  
Yiwen Wang
Keyword(s):  
State Estimation ◽  
Measurement Model ◽  
High Dimensional ◽  
Neural Firing ◽  
The Neural Network ◽  
Information Filter ◽  
Critical Technology ◽  
Dimensional Measurements ◽  
Non Gaussian ◽  
Low Dimensional

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.

PSS Business Case Map: Supporting Idea Generation in PSS Design

2012 ◽  
Author(s):  
Fumiya Akasaka ◽  
Kazuki Fujita ◽  
Yoshiki Shimomura
Keyword(s):  
Idea Generation ◽  
Business Case ◽  
Literature Survey ◽  
The Self ◽  
High Dimensional ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Service Type ◽  
Business Cases ◽  
Low Dimensional

This paper proposes the PSS Business Case Map as a tool to support designers’ idea generation in PSS design. The map visualizes the similarities among PSS business cases in a two-dimensional diagram. To make the map, PSS business cases are first collected by conducting, for example, a literature survey. The collected business cases are then classified from multiple aspects that characterize each case such as its product type, service type, target customer, and so on. Based on the results of this classification, the similarities among the cases are calculated and visualized by using the Self-Organizing Map (SOM) technique. A SOM is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional (typically two-dimensional) view from high-dimensional data. The visualization result is offered to designers in a form of a two-dimensional map, which is called the PSS Business Case Map. By using the map, designers can figure out the position of their current business and can acquire ideas for the servitization of their business.

scholarly journals Rate Equation Modelling of Nonlinear Dynamics in Directly Modulated Multiple Quantum Well Laser Diodes

VLSI Design ◽  
1998 ◽  
Vol 8 (1-4) ◽  
pp. 355-360 ◽  
Author(s):  
Stephen Bennett ◽  
Christopher M. Snowden ◽  
Stavros Iezekiel
Keyword(s):  
Nonlinear Dynamics ◽  
Quantum Well ◽  
Multiple Quantum Well ◽  
Period Doubling ◽  
Rate Equations ◽  
Multiple Quantum ◽  
Quantum Well Laser ◽  
Wide Range ◽  
Multiple Quantum Well Laser ◽  
Good Agreement

A theoretical (using rate equations) and experimental study of the nonlinear dynamics of a distributed feedback multiple quantum well laser diode is presented. The analysis is performed under direct modulation. Period doubling and period tripling are identified in both the measurements and simulations. Period doubling is found over a wide range of modulation frequencies in the laser. Computational results using rate equations show good agreement with the experimental results.

scholarly journals Perspectives of the high-dimensional dynamics of neural microcircuits from the point of view of low-dimensional readouts

Complexity ◽  
2003 ◽  
Vol 8 (4) ◽  
pp. 39-50 ◽  
Author(s):  
Stefan Häusler ◽  
Henry Markram ◽  
Wolfgang Maass
Keyword(s):  
Point Of View ◽  
High Dimensional ◽  
Low Dimensional

Lid-Driven Square Cavity Flow: A Benchmark Solution with an 8192x8192 Grid

2021 ◽  
Author(s):  
Carlos Marchi ◽  
Cosmo D. Santiago ◽  
Carlos Alberto Rezende de Carvalho Junior
Keyword(s):  
Reynolds Number ◽  
Stokes Equations ◽  
Accurate Determination ◽  
Stable Solution ◽  
Reynolds Numbers ◽  
Accurate Solution ◽  
Discretization Error ◽  
Square Cavity ◽  
Solution Vector ◽  
Wide Range

Abstract The incompressible steady-state fluid flow inside a lid-driven square cavity was simulated using the mass conservation and Navier-Stokes equations. This system of equations is solved for Reynolds numbers of up to 10,000 to the accuracy of the computational machine round-off error. The computational model used was the second-order accurate finite volume method. A stable solution is obtained using the iterative multigrid methodology with 8192 × 8192 volumes, while degree-10 interpolation and Richardson extrapolation were used to reduce the discretization error. The solution vector comprised five entries of velocities, pressure, and location. For comparison purposes, 65 different variables of interest were chosen, such as velocity profile, its extremum values and location, extremum values and location of the stream function. The discretization error for each variable of interest was estimated using two types of estimators and their apparent order of accuracy. The variations of the 11 selected variables are shown across 38 Reynolds number values between 0.0001 and 10,000. In this study, we provide a more accurate determination of the Reynolds number value at which the upper secondary vortex appears. The results of this study were compared with those of several other studies in the literature. The current solution methodology was observed to produce the most accurate solution till date for a wide range of Reynolds numbers.

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