Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows

arXiv:2404.03240v1 Announce Type: new
Abstract: Physics-informed neural networks (PINNs) have gained significant prominence as a powerful tool in the field of scientific computing and simulations. Their ability to seamlessly integrate physical principles into deep learning architectures has revolutionized the approaches to solving complex problems in physics and engineering. However, a persistent challenge faced by mainstream PINNs lies in their handling of discontinuous input data, leading to inaccuracies in predictions. This study addresses these challenges by incorporating the discretized forms of …

abstract architectures arxiv computing convolutional neural network cs.lg deep learning knowledge network networks neural network neural networks physics physics.flu-dyn physics-informed prediction scientific simulation simulations the simulation tool type