Results 141 to 150 of about 478,588 (342)

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

A new nonlinear dimensionality reduction method with application to hyperspectral image analysis [PDF]

, 2007
Shen‐En Qian, Guangyi Chen
openalex   +1 more source

Dimensionless Investigations on Energy Conversion and Analysis of Interlayer Time in Laser‐Based Powder Bed Fusion of Polymers for Polyamide 12 with Nanoadditives and Polypropylene

Advanced Engineering Materials, EarlyView.
This study explores the energy conversion in powder bed fusion of polymers using laser beam for polyamide 12 and polypropylene powders. It combines material and process data, using dimensionless parameters and numerical models, to enable the prediction of suitable printing parameters.
Christian Schlör, Moritz Grünewald, Kevin Popp, Ihsan Murat Kusoglu, Stephan Barcikowski, Stan Gann, Thomas Schuffenhauer, Martin Bastian, Claas Bierwisch, Johannes Rudloff   +9 more
wiley   +1 more source

Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators [PDF]

, 2021
Zhe Bai, Liqian Peng
openalex   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Quantum locally linear embedding for nonlinear dimensionality reduction [PDF]

arXiv, 2019
Reducing the dimension of nonlinear data is crucial in data processing and visualization. The locally linear embedding algorithm (LLE) is specifically a representative nonlinear dimensionality reduction method with well maintaining the original manifold structure.
arxiv  

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

European Journal of Remote Sensing, 2018
Managing transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary.
Giorgio Licciardi, Jocelyn Chanussot
doaj   +1 more source

Variational Dimension Reduction in Nonlinear Elasticity: A Young Measure Approach [PDF]

, 2008
Lorenzo Freddi, Roberto Paroni
openalex   +1 more source

Enhancing Corrosion Resistance and Mechanical Strength of 3D‐Printed Iron Polylactic Acid for Marine Applications via Laser Surface Texturing

Advanced Engineering Materials, EarlyView.
Laser surface texturing significantly improves the corrosion resistance and mechanical strength of 3D‐printed iron polylactic acid (Ir‐PLA) for marine applications. Optimal laser parameters reduce corrosion by 80% and enhance tensile strength by 25% and ductility by 15%.
Mohammad Rezayat, Mojtaba Karamimoghadam, Mohammadreza Lalegani Dezaki, Ali Zolfagharian, Giuseppe Casalino, Antonio Mateo, Mahdi Bodaghi   +6 more
wiley   +1 more source

Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets [PDF]

The quest for simplification in physics drives the exploration of concise mathematical representations for complex systems. This Dissertation focuses on the concept of dimensionality reduction as a means to obtain low-dimensional descriptions from high-dimensional data, facilitating comprehension and analysis.
arxiv   +1 more source