{"ID":2849442,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.22976","arxiv_id":"2510.22976","title":"Analysis of accuracy and efficiency of neural networks to simulate Navier-Stokes fluid flows with obstacles","abstract":"Conventional fluid simulations can be time consuming and energy intensive. We researched the viability of a neural network for simulating incompressible fluids in a randomized obstacle-heavy environment, as an alternative to the numerical simulation of the Navier-Stokes equation. We hypothesized that the neural network predictions would have a relatively low error for simulations over a small number of time steps, but errors would eventually accumulate to the point that the output would become very noisy. Over a rich set of obstacle configurations, we achieved a root mean square error of 0.32% on our training dataset and 0.36% on a testing dataset. These errors only grew to 1.45% and 2.34% at t = 10 and, 2.11% and 4.16% at timestep t = 20. We also found that our selected neural network was approximately 8,800 times faster at predicting the flow than a conventional simulation. Our findings indicate neural networks can be extremely useful at simulating fluids in obstacle-heavy environments. Useful applications include modeling forest fire smoke, pipe fluid flow, and underwater/flood currents.","short_abstract":"Conventional fluid simulations can be time consuming and energy intensive. We researched the viability of a neural network for simulating incompressible fluids in a randomized obstacle-heavy environment, as an alternative to the numerical simulation of the Navier-Stokes equation. We hypothesized that the neural network...","url_abs":"https://arxiv.org/abs/2510.22976","url_pdf":"https://arxiv.org/pdf/2510.22976v1","authors":"[\"Rui Hespanha\",\"Elliot McGuire\",\"João Hespanha\"]","published":"2025-10-27T03:57:29Z","proceeding":"physics.flu-dyn","tasks":"[\"physics.flu-dyn\",\"cs.LG\"]","methods":"[]","has_code":false}