ArticleScience advances2025
AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold networks.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception.Scientific reports · 2026Article
- MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.Science advances · 2026Article
- Robust MR-AIV: A Systematic Study of Robustness Improvement and Sensitivity Analysis of MR-AIV.bioRxiv : the preprint server for biology · 2026Article
- A variational framework for residual-based adaptivity in neural PDE solvers and operator learning.NPJ artificial intelligence · 2026Article
- KKANs: Ku̇rková-Kolmogorov-Arnold networks and their learning dynamics.Neural networks : the official journal of the International Neural Network Society · 2025Article
- Reconstruction of thermally-driven flows using Lagrangian particle data assimilation.Scientific reports · 2025Article
- MR-AIV revealsbioRxiv : the preprint server for biology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
We propose the artificial intelligence velocimetry-thermometry (AIVT) method to reconstruct a continuous and differentiable representation of the temperature and velocity in turbulent convection from measured three-dimensional (3D) velocity data. AIVT is based on physics-informed Kolmogorov-Arnold networks and trained by optimizing a loss function that minimizes residuals of the velocity data, boundary conditions, and governing equations. We apply AIVT to a set of simultaneously measured 3D temperature and velocity data of Rayleigh-Bénard convection, obtained by combining particle image thermometry and Lagrangian particle tracking. This enables us to directly compare machine learning results to true volumetric, simultaneous temperature and velocity measurements. We demonstrate that AIVT can reconstruct and infer continuous, instantaneous velocity and temperature fields and their gradients from sparse experimental data at a high resolution, providing an additional approach for understanding thermal turbulence.
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Registered trials
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