most citedPhysics-Informed Graph-Mesh Networks for PDEs: A hybrid approach for complex problems

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cs.LG2024

Gaussian process interpolation with conformal prediction: methods and comparative analysis

Aurélien Pion, Emmanuel Vazquez

This article advocates the use of conformal prediction (CP) methods for Gaussian process (GP) interpolation to enhance the calibration of prediction intervals. We begin by illustra…

cs.LG2023★ 3 cited

Physics-Informed Graph Convolutional Networks: Towards a generalized framework for complex geometries

Marien Chenaud, José Alves, Frédéric Magoulès

Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep L…