Accelerating Simulation of Two-Phase Flows with Neural PDE Surrogates
Yoeri Poels, Koen Minartz, Harshit Bansal, Vlado Menkovski

TL;DR
This paper explores neural PDE surrogates to accelerate two-phase flow simulations, extending existing methods to complex geometries, and demonstrates up to 1000x speed-up with improved accuracy in modeling oil expulsion dynamics.
Contribution
It introduces geometry-aware neural PDE solvers for complex two-phase flow problems, enhancing speed and accuracy over previous methods.
Findings
Neural PDE surrogates achieve up to 1000x speed-up.
Extensions improve performance over baseline models.
Varying geometries significantly increase problem complexity.
Abstract
Simulation is a powerful tool to better understand physical systems, but generally requires computationally expensive numerical methods. Downstream applications of such simulations can become computationally infeasible if they require many forward solves, for example in the case of inverse design with many degrees of freedom. In this work, we investigate and extend neural PDE solvers as a tool to aid in scaling simulations for two-phase flow problems, and simulations of oil expulsion from a pore specifically. We extend existing numerical methods for this problem to a more complex setting involving varying geometries of the domain to generate a challenging dataset. Further, we investigate three prominent neural PDE solver methods, namely the UNet, DRN, and U-FNO, and extend them for characteristics of the oil-expulsion problem: (1) spatial conditioning on the geometry; (2) periodicity in…
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Taxonomy
TopicsModel Reduction and Neural Networks · Lattice Boltzmann Simulation Studies · Generative Adversarial Networks and Image Synthesis
