Axial-offset magnetic negative stiffness spring with high density and linearity

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 109989 - 109989

Опубликована: Янв. 1, 2025

Язык: Английский

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Transfer Learning-Enhanced Finite Element-Integrated Neural Networks

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110075 - 110075

Опубликована: Фев. 1, 2025

Язык: Английский

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Energy absorption performance of Kresling origami tubes under impact loading

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 282, С. 109682 - 109682

Опубликована: Авг. 29, 2024

Язык: Английский

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Design and Kinematic Analysis of Origami Honeycomb Metamaterials with One-DOF Radial Motion

Thin-Walled Structures, Год журнала: 2025, Номер unknown, С. 112978 - 112978

Опубликована: Янв. 1, 2025

Язык: Английский

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Multistable bi-modulus Kresling origami with superior foldability and stiffness: An analytical design model

Engineering Structures, Год журнала: 2025, Номер 330, С. 119955 - 119955

Опубликована: Фев. 27, 2025

Язык: Английский

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An inverse design framework for optimizing tensile strength of composite materials based on a CNN surrogate for the phase field fracture model

Composites Part A Applied Science and Manufacturing, Год журнала: 2025, Номер unknown, С. 108758 - 108758

Опубликована: Фев. 1, 2025

Язык: Английский

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Enhancing compressive mechanical properties of anisotropic shell-based architected materials

Composite Structures, Год журнала: 2025, Номер unknown, С. 119240 - 119240

Опубликована: Май 1, 2025

Язык: Английский

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A Physics-Informed Neural Network framework to investigate nonlinear and heterogenous shrinkage of drying plant cells

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 275, С. 109267 - 109267

Опубликована: Апрель 7, 2024

This paper introduces a novel Physics-Informed Neural Network-based (PINN-based) multi-domain computational framework to analyse nonlinear and heterogeneous morphological variations of plant cells during drying. Here, two distinct models are involved: PINN-MT simulate mass transfer; PINN-NS shrinkage. The coupled examine cellular changes resulting from moisture loss Firstly, the framework, in tandem with homogeneous conditions, operates parallel, allowing mutual parameters update between models. approach demonstrates ability approximate shrinkage within tissue, factoring influence surrounding cells. Secondly, non-uniform cell wall properties boundary conditions incorporated into this through domain decomposition. Inherent capabilities neural networks allow for seamless integration multiple domains, additional terms introduced at interfaces. shows capacity account drastic even under extreme drying which is key novelty has been challenging task existing traditional methods. Hence, proposed offers an innovative avenue understanding not only cells, but also soft matter general.

Язык: Английский

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Physics-informed deep learning for structural dynamics under moving load

International Journal of Mechanical Sciences, Год журнала: 2024, Номер unknown, С. 109766 - 109766

Опубликована: Окт. 1, 2024

Язык: Английский

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Quantification of gradient energy coefficients using physics-informed neural networks

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 273, С. 109210 - 109210

Опубликована: Март 25, 2024

Язык: Английский

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