PINNs for the Solution of the Hyperbolic Buckley-Leverett Problem with a Non-convex Flux Function
This addresses a notoriously difficult fluid flow problem in porous media, offering a novel PINN-based solution that accurately captures shock-fronts without additional regularization.
The paper tackles solving the hyperbolic Buckley-Leverett problem with a non-convex flux function using physics-informed neural networks (PINNs), achieving an L2 validation error of 2 x 10^-2 and an L2 loss of 1 x 10^-6.
The displacement of two immiscible fluids is a common problem in fluid flow in porous media. Such a problem can be posed as a partial differential equation (PDE) in what is commonly referred to as a Buckley-Leverett (B-L) problem. The B-L problem is a non-linear hyperbolic conservation law that is known to be notoriously difficult to solve using traditional numerical methods. Here, we address the forward hyperbolic B-L problem with a nonconvex flux function using physics-informed neural networks (PINNs). The contributions of this paper are twofold. First, we present a PINN approach to solve the hyperbolic B-L problem by embedding the Oleinik entropy condition into the neural network residual. We do not use a diffusion term (artificial viscosity) in the residual-loss, but we rely on the strong form of the PDE. Second, we use the Adam optimizer with residual-based adaptive refinement (RAR) algorithm to achieve an ultra-low loss without weighting. Our solution method can accurately capture the shock-front and produce an accurate overall solution. We report a L2 validation error of 2 x 10-2 and a L2 loss of 1x 10-6. The proposed method does not require any additional regularization or weighting of losses to obtain such accurate solution.