Physics‐Informed Neural Networks (PINNs) provide a framework for integrating physical laws with data. However, their application to Prognostics and Health Management (PHM) remains constrained by the limited uncertainty quantification (UQ) capabilities.
Ibai Ramirez +4 more
wiley +1 more source
Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow. [PDF]
Lu T +5 more
europepmc +1 more source
A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...
Qi Zheng +4 more
wiley +1 more source
ANN-based thermal analysis of 3D MHD hybrid nanofluid flow over a shrinking sheet via LMA. [PDF]
Imran M +5 more
europepmc +1 more source
In Situ Response Time Measurement of RTD Based on LCSR Method. [PDF]
Song Y, Liang Y, Zhang Z, Su G, Su M.
europepmc +1 more source
Predictive Constitutive Modelling of Oxidation-Induced Degradation in 2.5D Woven C/SiC Composites. [PDF]
Wu T +6 more
europepmc +1 more source
Modeling of Methanol Steam Reforming in a Monolith Reactor: Effects of Internal Diffusion, Substrate Parameters, and Operating Conditions. [PDF]
Li Y, Fang M, Zhang Q, Wang X, Lei X.
europepmc +1 more source
Fractional-order modelling and analytical solutions for MHD casson fluid flow in an inclined channel with heat and mass transfer. [PDF]
Alwuthaynani M +5 more
europepmc +1 more source
Analytical and numerical computations for bioconvective flow Maxwell nanofluid with variable thermal properties using homotopy analysis method and finite difference scheme. [PDF]
Alzahrani J.
europepmc +1 more source

