Physics-informed neural network algorithm for solving forward and inverse problems of variable-order space-fractional advection–diffusion equations

Wang, Shupeng; Zhang, Hui; Jiang, Xiaoyun

doi:10.1016/j.neucom.2023.03.032

Cited by 15 publications

(1 citation statement)

References 21 publications

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“…The RC implementation is memory‐intensive and might be difficult to scale to a grid 10‐fold smaller in each direction. An ML‐based super‐resolution generator for each patch or grid column based on adversarial (Leinonen et al., 2021) or diffusion (Wang et al., 2020) modeling might help with this issue, but may only give the appearance of accuracy, likely degrades interpretability, and would greatly add to computational requirements if implemented for all regions and simulation times.…”

Section: A23's Achievements and Upcoming Challengesmentioning

confidence: 99%

Old Dog, New Trick: Reservoir Computing Advances Machine Learning for Climate Modeling

Bretherton

2023

Geophysical Research Letters

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Physics‐informed machine learning (ML) applied to geophysical simulation is developing explosively. Recently, graph neural net and vision transformer architectures have shown 1–7 days global weather forecast skill superior to any conventional model with integration times over 1,000 times faster, but longer simulations rapidly degrade. ML that achieves high skill in both weather and climate applications is a tougher goal. This Commentary was inspired by Arcomano et al. (2023, https://doi.org/10.1029/2022GL102649), who show impressive progress toward that goal using hybrid ML, combining reservoir computing (RC) to a coarse‐grid climate model and coupling to a separate data‐driven RC model that interactively predicts sea‐surface temperature. This opens new horizons; where will the next ML breakthrough come from, and is conventional climate modeling about to be disrupted?

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Section: A23's Achievements and Upcoming Challengesmentioning

confidence: 99%