PDE Surrogate Modeling
5 papers with code • 0 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in PDE Surrogate Modeling
Vibrating Plates
Representative PDE Benchmarks
Most implemented papers
Learning Neural PDE Solvers with Parameter-Guided Channel Attention
The experiments also show several advantages of CAPE, such as its increased ability to generalize to unseen PDE parameters without large increases inference time and parameter count.
Towards Multi-spatiotemporal-scale Generalized PDE Modeling
Finally, we show promising results on generalization to different PDE parameters and time-scales with a single surrogate model.
Convolutional Neural Operators for robust and accurate learning of PDEs
bogdanraonic3/convolutionalneuraloperator
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NeurIPS 2023
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs.
Scalable Transformer for PDE Surrogate Modeling
BaratiLab/FactFormer
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NeurIPS 2023
These sub-functions are then evaluated and used to compute the instance-based kernel with an axial factorized scheme.
Learning to Predict Structural Vibrations
To quantify such trade-offs systematically and foster the development of methods, we present a benchmark on the task of predicting the vibration of harmonically excited plates.
