MuRFiV combines multi-resolution deep learning with finite-volume inductive bias to deliver stable, accurate long-term autoregressive predictions of spatiotemporal PDE dynamics, outperforming standard neural baselines.
Super-resolution reconstruction of turbulent flows with machine learning.Journal of Fluid Mechanics, 870:106–120
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A multi-scale CNN super-resolution model outperforms baseline CNN, attention CNN, and diffusion-based approaches in reconstructing fine-scale features from under-resolved atmospheric flow simulations on standard benchmarks.
citing papers explorer
-
A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction
MuRFiV combines multi-resolution deep learning with finite-volume inductive bias to deliver stable, accurate long-term autoregressive predictions of spatiotemporal PDE dynamics, outperforming standard neural baselines.
-
Enhancing the accuracy of under-resolved numerical simulations of atmospheric flows with super resolution
A multi-scale CNN super-resolution model outperforms baseline CNN, attention CNN, and diffusion-based approaches in reconstructing fine-scale features from under-resolved atmospheric flow simulations on standard benchmarks.