Results 211 to 220 of about 937,595 (329)

Characterization and Inverse Design of Stochastic Mechanical Metamaterials Using Neural Operators

Advanced Materials, EarlyView.
This study presents a DeepONet‐based machine learning framework for designing stochastic mechanical metamaterials with tailored nonlinear mechanical properties. By leveraging sparse but high‐quality experimental data from in situ micro‐mechanical tests, high predictive accuracy and enable efficient inverse design are achieved.
Hanxun Jin, Boyu Zhang, Qianying Cao, Enrui Zhang, Aniruddha Bora, Sridhar Krishnaswamy, George Em Karniadakis, Horacio D. Espinosa   +7 more
wiley   +1 more source

Classification of Dog Breeds Using Convolutional Neural Network Models and Support Vector Machine. [PDF]

Bioengineering (Basel)
Cui Y, Tang B, Wu G, Li L, Zhang X, Du Z, Zhao W.   +6 more
europepmc   +1 more source

On-Line Support Vector Machine Regression

, 2002
Mario Martín
openalex   +1 more source

Support vector machines experts for time series forecasting [PDF]

, 2003
Lijuan Cao
openalex   +1 more source

Materials Advances in Devices for Heart Disease Interventions

Advanced Materials, EarlyView.
This review examines the crucial role of materials in heart disease interventions, focusing on strategies for monitoring, managing, and repairing heart conditions. It discusses the material requirements for medical devices, highlighting recent innovations and their impact on cardiovascular health.
Gagan K. Jalandhra, Lauryn Srethbhakdi, James Davies, Chi Cong Nguyen, Phuoc Thien Phan, Zachary Och, Aditya Ashok, Khoon S. Lim, Hoang‐Phuong Phan, Thanh Nho Do, Nigel H. Lovell, Jelena Rnjak‐Kovacina   +11 more
wiley   +1 more source

Support Vector Machine for Stratification of Cognitive Impairment Using 3D T1WI in Patients with Type 2 Diabetes Mellitus. [PDF]

Diabetes Metab Syndr Obes
Xu Z, Zhao L, Yin L, Cao M, Liu Y, Gu F, Liu X, Zhang G.   +7 more
europepmc   +1 more source

Bio-medical entity extraction using Support Vector Machines [PDF]

, 2003
Koichi Takeuchi, Nigel Collier
openalex   +1 more source

Designing Maximal Strength in Nanolamellar Eutectic High‐Entropy Alloys

Advanced Materials, EarlyView.
This study uses molecular dynamics simulations to guide the design of EHEAs with superior mechanical performance. The simulations reveal a peak tensile strength at a critical interphase boundary spacing. Below this spacing, the governing mechanism shifts from the Hall–Petch strengthening to dislocation multiplication–mediated softening. Guided by these
Weiming Ji, Shubo Gao, Asker Jarlöv, Xiaojun Shen, Yujia Tian, Mao See Wu, Huajian Gao, Kun Zhou   +7 more
wiley   +1 more source

Research on Sleep Staging Based on Support Vector Machine and Extreme Gradient Boosting Algorithm. [PDF]

Nat Sci Sleep
Wang Y, Ye S, Xu Z, Chu Y, Zhang J, Yu W.   +5 more
europepmc   +1 more source

Training of Support Vector Machines by the Steepest Ascent Method

, 2002
Shigeo Abe, Youichi Hirokawa, Seiichi Ozawa   +2 more
openalex   +2 more sources