Results 101 to 110 of about 2,349 (245)

Micro‐Mechanism Informed Neural Networks for Process‐Property Prediction in Laser Powder Bed Fusion

Artificial Intelligence for Engineering, EarlyView.
Hard physics embedding, where neural networks learn residuals relative to analytical baselines, substantially outperforms soft loss‐function constraints for extrapolation in LPBF process–property prediction. Physics integration architecture determines generalization capability more than constraint quantity.
Yo‐Lun Yang
wiley   +1 more source

Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis

IEEE Access
This paper focus on the application of Physics Informed Neural Network (PINN) for extracting parameters of photovoltaic (PV), wind, and energy storage equipment models.
Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta   +5 more
doaj   +1 more source

Inferring processes within dynamic forest models using hybrid modelling

Methods in Ecology and Evolution, EarlyView.
Abstract Modelling forest dynamics under novel climatic conditions requires a careful balance between process‐based understanding and empirical flexibility. Dynamic vegetation models (DVM) represent ecological processes mechanistically, but their performance is sensitive to misspecified functional forms and to unavoidable structural simplifications ...
Maximilian Pichler, Yannek Käber
wiley   +1 more source

Reconstruction of the pressure field in dense granular flow using physics-informed neural network

Engineering Applications of Computational Fluid Mechanics
Granular flows are extensively witnessed in natural environments such as dunes in deserts and avalanches, and industrial applications such as transporting cement and grains. Measuring the inherent pressure fields is usually challenging in such flows.
Yue Hu, Yinghui Wu, Congying Li, Daokun Zhang, Jiang Chen, Aibing Yu, Qijun Zheng   +6 more
doaj   +1 more source

Reduced order modelling of air‐puff test for corneal material characterisation

Journal of Microscopy, EarlyView.
Abstract Models of the fluid–structure interaction (FSI) model for the air‐puff test were analysed. Using Abaqus, the air‐puff test is applied to eyes with varying biomechanical parameters, such as material properties, corneal thickness, and radius.
Osama M. Maklad, Muting Hao
wiley   +1 more source

Complex-valued physics-informed machine learning for efficient solving of quintic nonlinear Schrödinger equations

Physical Review Research
The Gross-Pitaevskii equation (GPE), a specialized form of the nonlinear Schrödinger equation (NLSE), plays a pivotal role in quantum mechanics, optics, and condensed matter physics, modeling phenomena such as superfluidity, quantum turbulence, and ...
Lei Zhang, Mengge Du, Xiaodong Bai, Yuntian Chen, Dongxiao Zhang   +4 more
doaj   +1 more source

Data-Driven and Physics-Informed Neural Networks for Structural Health Monitoring of the Z24 Bridge

Journal of the Civil Engineering Forum
Structural Health Monitoring (SHM) is crucial for maintaining the sustainability and safety of civil infrastructure. The Z24 Bridge in Switzerland remains one of the benchmark datasets used to validate vibration-based damage detection methods ...
Abdellah Riyahi, Mohammed Mestari, Bouchra Bouihi   +2 more
doaj   +1 more source

Hyperspectral tomographic diffractive microscopy: Development and applications

Journal of Microscopy, EarlyView.
Abstract Tomographic Diffractive Microscopy (TDM) provides label‐free three‐dimensional imaging of transparent samples with resolution surpassing confocal limits. At IRIMAS, successive instrumental developments since 2009 have enhanced TDM capabilities through transmission, reflection, isotropic, polarisation‐sensitive, and dual‐view configurations ...
Leonardo Pestana Legori, Carlos Alberto Chacón Ávila, Matthieu Debailleul, Nicolas Verrier, Olivier Haeberlé   +4 more
wiley   +1 more source

Error estimates for a physics-informed neural network in solving KdV equations

Machine Learning: Science and Technology
This paper aims to provide error bounds on physics-informed neural network (PINN) in solving Korteweg–de Vries (KdV) equations. We prove that a neural network equipped with two hidden layers and the tanh activation function can reduce the partial ...
Jia Guo, Ziyuan Liu, Chenping Hou
doaj   +1 more source

Physics-Informed Neural Network for Nonlinear Bending Analysis of Nano-Beams: A Systematic Hyperparameter Optimization

Mathematical and Computational Applications
This paper investigates the nonlinear bending analysis of nano-beams using the physics-informed neural network (PINN) method. The nonlinear governing equations for the bending of size-dependent nano-beams are derived from Hamilton’s principle ...
Saba Sadat Mirsadeghi Esfahani, Ali Fallah, Mohammad Mohammadi Aghdam   +2 more
doaj   +1 more source