arXiv:2605.20250v1 Announce Type: new Abstract: Accurate simulation of fluid flow in porous media is challenging due to complex pore-space geometries and the computational cost of solving the Navier-Stokes equations. This difficulty is particularly important when repeated simulations are required, as standard numerical solvers may converge slowly in intricate porous domains. We present a neural-network-based framework for predicting pore-scale velocity fields directly from sample geometry. The method uses a convolutional encoder-decoder architecture with skip connections to preserve spatial de
The increasing maturity of AI, particularly convolutional neural networks, coupled with demand for more efficient simulation methods, enables this application to complex physics problems now.
This development represents a significant step towards faster, more accurate, and computationally less expensive fluid dynamic simulations, critical for various industrial and environmental applications.
The reliance on traditional numerical solvers for porous media fluid flow can be significantly reduced, leading to accelerated design cycles and predictive capabilities in subsurface engineering.
- · Oil & Gas Industry
- · Environmental Engineering
- · AI/ML researchers in fluid dynamics
- · Computational Scientists
- · Developers of traditional Navier-Stokes solvers
Reduced computational costs and time for simulating complex fluid flows in porous media.
Improved efficiency in reservoir engineering, carbon sequestration, and groundwater management due to better predictive models.
Acceleration of research and development in new materials with optimized porous structures for filtration or energy storage applications.
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