Results 31 to 40 of about 478,588 (342)
IMMERSIVE VISUALIZATION OF THE QUALITY OF DIMENSIONALITY REDUCTION [PDF]
Dimensionality reduction is the most widely used approach for extracting the most informative low-dimensional features from highdimensional ones. During the last two decades, different techniques (linear and nonlinear) have been proposed by researchers ...
M. Babaee, M. Datcu, G. Rigoll
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This study aims to explore the feasibility of using a structure inspired by the features of horsetail and human spine as the potential helmet liner, targeting at mitigation of acceleration‐induced injuries. A parametric study is conducted to investigate the effect of individual geometrical variables in the design, indicating its capability to reduce ...
Bing Leng+3 more
wiley +1 more source
Analyzing Grid-Based Direct Quantum Molecular Dynamics Using Non-Linear Dimensionality Reduction
Grid-based schemes for simulating quantum dynamics, such as the multi-configuration time-dependent Hartree (MCTDH) method, provide highly accurate predictions of the coupled nuclear and electronic dynamics in molecular systems.
Gareth W. Richings, Scott Habershon
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A Variational Beam Model for Failure of Cellular and Truss‐Based Architected Materials
Herein, a versatile and efficient beam modeling framework is developed to predict the nonlinear response and failure of cellular, truss‐based, and woven architected materials. It enables the exploration of their design space and the optimization of their mechanical behavior in the nonlinear regime. A variational formulation of a beam model is presented
Konstantinos Karapiperis+3 more
wiley +1 more source
Architected Lattices with a Topological Transition
This article develops topological metamaterials showing multidirectional two‐step deformation under compression by embedding contact‐enabled topological mechanisms into lattice structures. Experiments on 3D‐printed 2D and 3D lattices and finite element simulations are conducted to demonstrate the working principle of the topological metamaterials.
Shivam Agarwal, Lihua Jin
wiley +1 more source
Quantum diffusion map for nonlinear dimensionality reduction [PDF]
Inspired by random walk on graphs, diffusion map (DM) is a class of unsupervised machine learning that offers automatic identification of low-dimensional data structure hidden in a high-dimensional dataset. In recent years, among its many applications, DM has been successfully applied to discover relevant order parameters in many-body systems, enabling
Apimuk Sornsaeng+3 more
openaire +2 more sources
Learning Neural Representations and Local Embedding for Nonlinear Dimensionality Reduction Mapping
This work explores neural approximation for nonlinear dimensionality reduction mapping based on internal representations of graph-organized regular data supports.
Sheng-Shiung Wu+3 more
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Improving Dimensionality Reduction Projections for Data Visualization
In data science and visualization, dimensionality reduction techniques have been extensively employed for exploring large datasets. These techniques involve the transformation of high-dimensional data into reduced versions, typically in 2D, with the aim ...
Bardia Rafieian+2 more
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The human hand needs a large number of sensors to measure kinematics owing to its large number of degrees of freedom. Existing devices like data gloves and optical trackers are associated with calibration, line of sight, and accuracy problems.
Prajwal Shenoy+2 more
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Analytical Phase Reduction for Weakly Nonlinear Oscillators [PDF]
Phase reduction is a dimensionality reduction scheme to describe the dynamics of nonlinear oscillators with a single phase variable. While it is crucial in synchronization analysis of coupled oscillators, analytical results are limited to few systems. In this letter, we analytically perform phase reduction for a wide class of oscillators by extending ...
arxiv +1 more source