Results 61 to 70 of about 8,116 (238)

Deflection of foundation pit retaining wall in heterogeneous soil strata: Intelligent prediction methods based on physics-informed machine learning

Journal of Rock Mechanics and Geotechnical Engineering
Excavation-induced retaining wall deflection (RWD) significantly influences the safety of surrounding built environment. To predict the three-dimensional RWD in heterogeneous strata, a new partial differential equation (PDE) is derived in this study, and
Cong Zhou, Lei He, Junchen He, Yi Zhang, Huaiguang Xiao, Chee Kiong Soh   +5 more
doaj   +1 more source

Bridging the translational gap in clinical nanomedicine: From rational design to clinical reality

BMEMat, EarlyView.
Nanotechnology offers a multi‐faceted approach to modern healthcare, encompassing high‐sensitivity diagnostic imaging, innovative vaccine delivery, and targeted therapeutics. This review explores how these nanomedicine applications address critical challenges in cancer recurrence and the treatment of neurologic and respiratory diseases to combat rising
Ahmed H. Ghonaim, Marwa Ahmed El‐Naka, Wedad M. Nageeb, Atta Ullah Khan, Noura M. Seleem, Aliaa Abdelghafar, Doaa Zamel, Yang Liu, Qigai He, Wentao Li   +9 more
wiley   +1 more source

Electrospun Porous Materials for the Next‐Generation Proton Exchange Membrane Fuel Cells: Progress, Challenges, and AI‐Optimized Perspectives

Carbon Energy, EarlyView.
Incorporating AI to optimize solution, process, and environment enables efficient prediction of structural properties and performance, allowing tailoring of GDL, CL, and PEM fabrication to guide PEMFC toward 9.0 kW L‒1 by 2040. ABSTRACT The performance and commercial viability of proton exchange membrane fuel cells (PEMFCs) are constrained by key ...
Shangfeng Jiang, Panfeng Wang, You Gong, Zeyu Sun, Huatan Chen, Geng Wang, Ting Tao, Chuang Zhai, Xi Wang, Ruixin Chen, Jiaxin Jiang, Bingrong Zhang   +11 more
wiley   +1 more source

R-PINN: Recovery-type a-posteriori estimator enhanced adaptive PINN

Journal of Computational Physics
In recent years, with the advancements in machine learning and neural networks, algorithms using physics-informed neural networks (PINNs) to solve PDEs have gained widespread applications. While these algorithms are well-suited for a wide range of equations, they often exhibit suboptimal performance when applied to equations with large local gradients,
Rongxin Lu, Jiwei Jia, Young Ju Lee, Zheng Lu, Chen-Song Zhang   +4 more
openaire   +3 more sources

A dynamic mesh refinement in Physic-Informed Neural Networks for handling Stiff Partial Differential Equations

Alexandria Engineering Journal
Stiff Partial Differential Equations (PDEs) pose considerable challenges due to the presence of steep gradients, which often lead to large residuals when employing coarse training mesh grids, while excessively refined grids substantially increase ...
Nursyiva Irsalinda, Maharani A. Bakar, Fatimah Noor Harun, Ruwaidiah Idris, Sugiyarto Surono   +4 more
doaj   +1 more source

AI‐Enabled Precision Dosing in Pediatrics: Enhancing Model‐Informed Decision Making

Clinical Pharmacology &Therapeutics, EarlyView.
Ensuring safe and effective pharmacotherapy for children remains a central challenge in clinical pharmacology, yet rapid advances in AI have not translated into clinical practice. This Perspective highlights how AI‐enabled approaches can enhance model‐informed decision making for precision dosing.
Kei Irie, Tomoyuki Mizuno
wiley   +1 more source

Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture [PDF]

, 2023
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.
Han, Xiaotian, Braga-Neto, Ulisses, Chang, Chia-Yuan, Zha, Daochen, Hu, Xia, Wang, Yicheng   +5 more
core   +1 more source

On the Hard Boundary Constraint Method for Fluid Flow Prediction based on the Physics-Informed Neural Network

Applied Sciences
With the rapid development of artificial intelligence technology, the physics-informed neural network (PINN) has gradually emerged as an effective and potential method for solving N-S equations.
Zixu Xiao, Yaping Ju, Zhen Li, Jiawang Zhang, Chuhua Zhang   +4 more
doaj   +1 more source

Physics-informed neural networks for solving forward and inverse Vlasov–Poisson equation via fully kinetic simulation

Machine Learning: Science and Technology, 2023
The Vlasov–Poisson equation is one of the most fundamental models in plasma physics. It has been widely used in areas such as confined plasmas in thermonuclear research and space plasmas in planetary magnetospheres.
Baiyi Zhang, Guobiao Cai, Huiyan Weng, Weizong Wang, Lihui Liu, Bijiao He   +5 more
doaj   +1 more source

Multi‐factor coupling effects in hydraulic fracturing of laminated shale: Experimental insights and physics‐informed neural network‐driven optimization

Deep Underground Science and Engineering, EarlyView.
This study establishes a multi‐factor coupling framework for predicting breakdown pressure in laminated shale by integrating experimental hydraulic fracturing tests, physics‐informed neural networks (PINNs), and Sobol sensitivity analysis. It reveals how differential stress, the bedding dip angle, and the injection rate interact to influence fracture ...
Tao Wang, Guoyao Xu, Liyuan Liu, Yao Zhang, Sitao Zhu, Hongyang Chu, Kaikai Zhao   +6 more
wiley   +1 more source