Results 1 to 10 of about 80,482 (270)

PHYSICS-INFORMED NEURAL NETWORKS FOR NARROWBAND SIGNAL PROPAGATION MODELING

Електроніка та інформаційні технології
Background. Physics-informed neural networks (PINN) demonstrated strong capabilities in solving direct and inverse problems for partial differential equations.
Igor Kolych
doaj   +1 more source

Computation of waveguide eigenmodes by physics-informed neural networks

Machine Learning. Engineering
Physics-informed neural networks (PINNs) have emerged as powerful deep-learning frameworks for solving partial differential equations by directly embedding physical laws into the learning process.
Geetanjli, Kirankumar R Hiremath
doaj   +1 more source

Forecasting secular variation using physics-informed neural networks for IGRF-14. [PDF]

Earth Planets Space
Shakespeare-Rees N, Livermore PW, Davies CJ, Rogers HF, Beggan CD, Brown WJ, Finlay CC.   +6 more
europepmc   +1 more source

Implementing physics-informed neural networks with deep learning for differential equations. [PDF]

Front Artif Intell
Emmert-Streib F, Tripathi S, Farea A, Holzinger A.   +3 more
europepmc   +1 more source

Peristaltic transport and thermodynamic analysis of hybrid nanofluids in porous media using physics-informed neural networks. [PDF]

Discov Nano
Vaseem M, Uddin Z, Upreti H.
europepmc   +1 more source

A Nonlinear Error Compensation Method for Heterodyne Interferometry Based on Self-Supervised Physics-Informed Neural Networks with Frequency-Domain Priors. [PDF]

Sensors (Basel)
Wang Y, Sun H, Li J, Ma C, Zhang Y, Wang X, Feng Q.   +6 more
europepmc   +1 more source

Low-Temperature Prediction in Commercial Lithium-Ion Batteries during Dynamic Usage via Enhanced Physics-Informed Neural Networks. [PDF]

ACS Omega
Pereira EL, Soares DM.
europepmc   +1 more source

Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow. [PDF]

J Comput Nonlinear Dyn
Lu T, Shahadat MRB, Liu Q, He R, Jiang X, Li Z.   +5 more
europepmc   +1 more source

Investigating the use of physics informed neural networks for dam-break scenarios. [PDF]

PLoS One
Mumtaz K, Nadeem MW, Khan A, Lakdawala Z.   +3 more
europepmc   +1 more source

Evaluating physics informed neural networks for water contamination risk prediction and environmental sustainability. [PDF]

Sci Rep
Rashid S, Abbas H, Ali I, Khalid A, Tesgera Agama F, Aldien MS.   +5 more
europepmc   +1 more source