A Library for Learning Neural Operators
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Built on top of PyTorch, NeuralOperator provides all the tools for training and deploying neural operator models, as well as developing new ones, in a high-quality, tested, open-source package. It combines cutting-edge models and customizability with a gentle learning curve and simple user interface for newcomers.
Code (1)
Tasks
Operator learningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
HyperNOs: Automated and Parallel Library for Neural Operators Research
This paper introduces HyperNOs, a PyTorch library designed to streamline and automate the process of exploring neural operators, with a special focus on hyperparameter optimization for comprehensive and exhaustive explor…
Hyperparameter OptimizationDifferentiable Computational Geometry for 2D and 3D machine learning
With the growth of machine learning algorithms with geometry primitives, a high-efficiency library with differentiable geometric operators are desired. We present an optimized Differentiable Geometry Algorithm Library (D…
BIG-bench Machine LearningGPUDiffSharp: An AD Library for .NET Languages
DiffSharp is an algorithmic differentiation or automatic differentiation (AD) library for the .NET ecosystem, which is targeted by the C# and F# languages, among others. The library has been designed with machine learnin…
GPUDo Neurons Dream of Primitive Operators? Wake-Sleep Compression Rediscovers Schank's Event Semantics
We show that they do. Roger Schank's conceptual dependency theory proposed that all human events decompose into primitive operations -- ATRANS (transfer of possession), PTRANS (physical movement), MTRANS (information tra…
Nonlinear integro-differential operator regression with neural networks
This note introduces a regression technique for finding a class of nonlinear integro-differential operators from data. The method parametrizes the spatial operator with neural networks and Fourier transforms such that it…
regression