Knowledge-Based Convolutional Neural Network for the Simulation and Prediction of Two-Phase Darcy Flows
This work addresses inaccuracies in PINN simulations for fluid dynamics, offering an incremental improvement for researchers in scientific computing and engineering.
The study tackled the challenge of handling discontinuous input data in physics-informed neural networks (PINNs) for simulating two-phase Darcy flows, resulting in improved accuracy and reduced computational cost compared to traditional methods.
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 the governing equations into the PINN framework. We propose to combine the power of neural networks with the dynamics imposed by the discretized differential equations. By discretizing the governing equations, the PINN learns to account for the discontinuities and accurately capture the underlying relationships between inputs and outputs, improving the accuracy compared to traditional interpolation techniques. Moreover, by leveraging the power of neural networks, the computational cost associated with numerical simulations is substantially reduced. We evaluate our model on a large-scale dataset for the prediction of pressure and saturation fields demonstrating high accuracies compared to non-physically aware models.