Code required to reproduce results presented in "Probabilistic Operator Learning for Climate Model Parameterisation"
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Updated
Jun 29, 2024 - Jupyter Notebook
Code required to reproduce results presented in "Probabilistic Operator Learning for Climate Model Parameterisation"
Fokker Planck based Data Assimilation method using Fourier Neural Operators as integrator
Project Portfolio
RenONet: Multiscale operator learning for complex social systems
Hyperbolic Learning Rate Scheduler
Code for the paper ``Error Bounds for Learning with Vector-Valued Random Features''
CODES (Coupled ODE Surrogates) aims to make surrogates for coupled ODE systems comparable and to aid in learning about their learning behaviour.
Graph Feedforward Networks: a resolution-invariant generalisation of feedforward networks for graphical data, applied to model order reduction
Nonlinear model reduction for operator learning
Code for the paper "The Random Feature Model for Input-Output Maps between Banach Spaces"
Official repo for separable operator networks -- extreme-scale operator learning for parametric PDEs.
An extension of Fourier Neural Operator to finite-dimensional input and/or output spaces.
Code for training and inferring acoustic wave propagation in 3D
Datasets and code for results presented in the BOON paper
Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.
PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.
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