Results 81 to 90 of about 2,291 (214)

Computational intelligence model for predicting the compressive strength of FRP‐confined concrete column

Structural Concrete, EarlyView.
Abstract Fiber reinforced polymer (FRP) wrapping technology is commonly used to enhance the compressive strength (CS) of reinforced concrete (RC) members. Accurate prediction of the compressive strength of FRP‐confined concrete columns is crucial for optimizing structural design and helps reduce the time and costs associated with physical testing ...
XuanRui Yu, Nima Khodadadi, Anxiang Song, Tianyu Hu, Yang Yu, Antonio Nanni   +5 more
wiley   +1 more source

Strategies for multi-case physics-informed neural networks for tube flows: a study using 2D flow scenarios

Scientific Reports
Fluid dynamics computations for tube-like geometries are crucial in biomedical evaluations of vascular and airways fluid dynamics. Physics-Informed Neural Networks (PINNs) have emerged as a promising alternative to traditional computational fluid ...
Hong Shen Wong, Wei Xuan Chan, Bing Huan Li, Choon Hwai Yap   +3 more
doaj   +1 more source

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

Data-driven solutions and parameters discovery of the Mukherjee–Kundu equation via physics-informed neural networks with adaptive sampling [PDF]

AIP Advances
In this work, data-driven solutions and parameter discovery for the Mukherjee–Kundu equation are considered using Physics-Informed Neural Networks (PINNs) with Residual-based Adaptive Distribution (RAD) sampling. For data-driven solutions, the equation’s
Wenxu Liu, Tong Li, Xinran Yuan, Jiani Guo, Ruigang Zhang   +4 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

PINNs for Electromagnetic Wave Propagation

CoRR
Physics-Informed Neural Networks (PINNs) solve physical systems by incorporating governing partial differential equations directly into neural network training. In electromagnetism, where well-established methodologies such as FDTD and FEM already exist, new methodologies are expected to provide clear advantages to be accepted.
openaire   +2 more sources

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

A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials

IEEE Access
This work explores the use of Physics-Informed Neural Networks (PINNs) and a newly proposed approach, called the STacked Adaptive Residual PINN (STAR-PINN), to solve magnetic diffusion problems in the magneto quasi static regime.
Shayan Dodge, Sami Barmada, Alessandro Formisano   +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

AI in chemical engineering: From promise to practice

AIChE Journal, Volume 72, Issue 7, July 2026.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source