Computational Simulation of Nanoparticle Behavior Using Artificial Neural Networks

Nanotechnology and computer modeling have come together to create new ways to explore targeted drug management and diagnostic devices in the human vascular system. This simulation uses Artificial Neural Networks (ANNs) to model the behavior of nanoparticles in hemodynamic contexts. ANNs can predict how nanoparticles will move, spread out, and group together in different blood flow situations since they can represent complex nonlinear systems. The simulation includes physical elements such as blood viscosity, wall shear stress (WSS), oscillatory shear index (OSI), relative residence time (RRT) and the characteristics of nanoparticles. The ANN was trained on a dataset obtained via numerical modeling to precisely predict the movements of nanoparticles. The findings indicate that the ANN model yields precise and computationally efficient predictions in contrast to conventional numerical simulations. This technology paves the way for personalized nanomedicine in the future, enabling real-time forecasting and control of nanoparticle usage, thereby enhancing treatment safety and success.