PARTIAL DIFFERENTIAL EQUATION (PDE) SOLVERS ENHANCED BY DEEP LEARNING FOR GENOMIC FLUID DYNAMICS
A hybrid architecture combining Physics-Informed Neural Networks (PINNs) with operator learning techniques to efficiently solve nonlinear PDEs governing blood flow in viscoelastic arteries, with emphasis on the influence of external magnetic fields and genomic-scale parameter variations is proposed.