arXiv Machine Learning June 18, 2026 By Xizhuo (Cici), Zhang, Zekai Wang, Fei Liu, Bing Yao

P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolution

Read the original on arXiv Machine Learning →

arXiv:2606. 19303v1 Announce Type: new Abstract: High-fidelity simulation of spatiotemporal dynamics is computationally prohibitive, necessitating efficient super-resolution techniques to reconstruct high-resolution data from coarse-grained inputs.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Latent PDE mapping for efficient physics-informed learning across geometries with limited data

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Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs.

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A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

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From Raw Segmentations to Simulation-Ready Cardiac Meshes: An Automated Framework for Anatomical Reconstruction and Virtual Cohort Generation

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arXiv Machine Learning

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By SiHun Lee, Dong-Hyuk Park, Taesoo Bang, Seung-Hoon Kang

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