Results 161 to 170 of about 8,116 (238)

AUKF-PINN: An adaptive framework for low-energy gamma multiphase flow measurement under noisy industrial conditions. [PDF]

PLoS One
Huang Y, Liang H, Zhu L, He L, Zeng T, Liu M.   +5 more
europepmc   +1 more source

A Hybrid and Comparative Machine Learning Framework for Predicting Gestational Diabetes Mellitus via a Prospective Case-Control Design: A Pilot Study Integrating Periodontal Health and Hematological Inflammatory Markers. [PDF]

Metabolites
Temur İ, Özsan M, Temur KT, Çolak AB.
europepmc   +1 more source

Physics-informed multi-task learning for permeability prediction and probabilistic HFU modeling: a case study from the Lower Bahariya Reservoir, Shahd SE field Egypt. [PDF]

Sci Rep
Saleh K, Mabrouk WM, Metwally AM.
europepmc   +1 more source

Going with the flow to solve for symmetry-driven PDE dynamics with physics-informed neural networks. [PDF]

Nat Commun
Kavousanakis ME, Fabiani G, Georgiou AS, Siettos C, Kevrekidis PG, Kevrekidis IG.   +5 more
europepmc   +1 more source

PINN for stiff moving-boundary PDE to predict the locking point in superheated steam drying. [PDF]

Sci Rep
Malekjani N, Bück A, Tsotsas E, Kharaghani A.   +3 more
europepmc   +1 more source

Physics-Informed Neural Networks for Predicting Hydration Degree in Cementitious Systems Blended with Slag and Fly Ash. [PDF]

Materials (Basel)
Hu X, Liao X, Liao Z, Wang Z, Gan M.
europepmc   +1 more source

A Diffusion-Based Digital Twin for Predicting Road Deterioration under Climate and Traffic Stress. [PDF]

Research (Wash D C)
Pan N, Du Y, Parlikad AK.
europepmc   +1 more source

Optical Scattering Characteristics of Three-Dimensional Targets Based on Transfer Learning PINNs. [PDF]

Micromachines (Basel)
Li R, Huang J, Tang H, Zhou C, Liu F, Wang Z.   +5 more
europepmc   +1 more source

Convergence and error analysis of PINNs

Physics-informed neural networks (PINNs) are a promising approach that combines the power of neural networks with the interpretability of physical modeling. PINNs have shown good practical performance in solving partial differential equations (PDEs) and in hybrid modeling scenarios, where physical models enhance data-driven approaches.
Doumèche, Nathan, Biau, Gérard, Boyer, Claire   +2 more
openaire   +1 more source

Physics-guided framework for climate dependent disease prediction: coupling compartment model with deep learning. [PDF]

BMC Public Health
Li T, Li W, Hu S.
europepmc   +1 more source