camlab-ethz/ConvolutionalNeuralOperator
This repository is the official implementation of the paper Convolutional Neural Operators for robust and accurate learning of PDEs
This project offers an advanced method for solving complex scientific and engineering problems described by Partial Differential Equations (PDEs). It takes data representing physical systems, like fluid dynamics or wave propagation, and accurately predicts their behavior or states. This tool is designed for researchers, scientists, and engineers who work with computational modeling and simulations in fields such as physics, mechanical engineering, or climate science.
210 stars.
Use this if you need a highly accurate and robust way to predict the outcomes of systems governed by PDEs, especially when traditional numerical methods are too slow or struggle with varying conditions.
Not ideal if you are looking for a simple, off-the-shelf solution without any experience in scientific computing or deep learning, as it requires some technical setup.
Stars
210
Forks
31
Language
Python
License
MIT
Category
Last pushed
Nov 24, 2025
Commits (30d)
0
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