PDE Surrogate Modeling
8 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in PDE Surrogate Modeling
DrivAerNet
Vibrating Plates
Representative PDE Benchmarks
HEMEW^S-3D
FSI
Most implemented papers
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.
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.
Learning to Predict Structural Vibrations
ecker-lab/learning_vibrating_plates
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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.
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.
Extreme time extrapolation capabilities and thermodynamic consistency of physics-inspired Neural Networks for the 3D microstructure evolution of materials via Cahn-Hilliard flow
dlanzo/CRANE
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A Convolutional Recurrent Neural Network (CRNN) is trained to reproduce the evolution of the spinodal decomposition process in three dimensions as described by the Cahn-Hilliard equation.
Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations
This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs).
Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems
maxxxzdn/erwin
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Large-scale physical systems defined on irregular grids pose significant scalability challenges for deep learning methods, especially in the presence of long-range interactions and multi-scale coupling.
