Results 51 to 60 of about 613,484 (243)

Low complex direction of arrival estimation method based on adaptive filtering algorithm

The Journal of Engineering, 2019
Conventional subspace decomposition-based direction of arrival (DOA) estimation methods require eigenvalue decomposition of spatial covariance matrix, therefore these methods are computationally intensive, and their implementation is difficult in real ...
Babur Jalal, Xiaopeng Yang, Denis Igambi, Tehseen Ul Hassan, Zeeshan Ahmad   +4 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A nonlocal eigenvalue problem and the stability of spikes for reaction-diffusion systems with fractional reaction rates [PDF]

, 2003
We consider a nonlocal eigenvalue problem which arises in the study of stability of spike solutions for reaction-diffusion systems with fractional reaction rates such as the Sel'kov model, the Gray-Scott system, the hypercycle Eigen and Schuster ...
Winter, M, Juncheng Wei, Matthias Winter, Wei, J   +3 more
core   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Graph Coloring Using Eigenvalue Decomposition

, 1983
Determining whether the vertices of a graph can be colored using $k$ different colors so that no two adjacent vertices receive the same color is a well-known NP-complete problem. Graph coloring is also of practical interest (for example, in estimating
Gilbert, John R., Aspvall, Bengt
core   +6 more sources

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Fast Phase Estimation Approach for Distributed Scatterer Based on Power Method

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Distributed scatterer (DS) possesses a medium signal-to-noise ratio, and its interferometric phase is typically estimated from sample covariance matrix (SCM) using phase linking algorithm. However, the phase estimation of DS is very time consuming.
Chuanguang Zhu, Yangqi Gao, Liya Zhang, Mengguang Liao, Wenhao Wu   +4 more
doaj   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Enhanced Phase Optimization Using Spectral Radius Constraints and Weighted Eigenvalue Decomposition for Distributed Scatterer InSAR

Remote Sensing
Eigenvalue decomposition (EVD) of covariance matrices or coherence matrices has been employed to suppress noise in phase information, and this approach has shown some effectiveness in data processing.
Jun Feng, Hongdong Fan, Yuan Yuan, Ziyang Liu   +3 more
doaj   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source