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Eikonal Regularisation in PINNs for 3D Fluid Dynamics Explored

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of Eikonal Regularisation in physics-informed neural networks (PINNs) for simulating three-dimensional fluid dynamics. The study, authored by Muhammad Akbar Khan, investigates how the weight of this regulariser impacts accuracy and reproducibility across various benchmarks, including translating, rotating, and slotted spheres. The findings indicate that the optimal weight is highly dependent on the flow's geometric complexity and that while PINNs offer a promising approach, a fifth-order WENO solver demonstrated superior accuracy in the tested scenarios. AI

IMPACT This research could lead to more accurate and reproducible simulations in fluid dynamics using AI.

RANK_REASON The cluster contains a research paper detailing a novel application of neural networks to a specific scientific problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Eikonal Regularisation in PINNs for 3D Fluid Dynamics Explored

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The cluster contains a research paper detailing a novel application of neural networks to a specific scientific problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Muhammad Akbar Khan ·

    Eikonal Regularisation in Physics-Informed Neural Networks for Three-Dimensional Level-Set Advection: Transferability of Two-Dimensional Design Principles

    arXiv:2608.08322v1 Announce Type: cross Abstract: Physics-informed neural networks applied to the level-set formulation of interface advection commonly augment the residual and initial-condition losses with an eikonal regulariser, penalising the deviation of $\|\nabla\phi\|$ from…

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