Operator learning

14 papers with code • 0 benchmarks • 0 datasets

Learn an operator between infinite dimensional Hilbert spaces or Banach spaces

Most implemented papers

Convolutional Analysis Operator Learning: Acceleration and Convergence

mechatoz/convolt 15 Feb 2018

This paper proposes a new convolutional analysis operator learning (CAOL) framework that learns an analysis sparsifying regularizer with the convolution perspective, and develops a new convergent Block Proximal Extrapolated Gradient method using a Majorizer (BPEG-M) to solve the corresponding block multi-nonconvex problems.

Convolutional Analysis Operator Learning: Dependence on Training Data

dahong67/ConvolutionalAnalysisOperatorLearning.jl 21 Feb 2019

Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.

Physics-Informed Neural Operator for Learning Partial Differential Equations

devzhk/PINO 6 Nov 2021

The Physics-Informed Neural Network (PINN) is an example of the former while the Fourier neural operator (FNO) is an example of the latter.

Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift

kazizzad/LabelShiftEstimator 29 Nov 2020

We deploy these estimators and provide generalization bounds in the unlabeled target domain.

Learning Symbolic Operators for Task and Motion Planning

ronuchit/LOFT_IROS_2021 28 Feb 2021

We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system.

Choose a Transformer: Fourier or Galerkin

scaomath/galerkin-transformer NeurIPS 2021

Without softmax, the approximation capacity of a linearized Transformer variant can be proved to be comparable to a Petrov-Galerkin projection layer-wise, and the estimate is independent with respect to the sequence length.

Multiwavelet-based Operator Learning for Differential Equations

gaurav71531/mwt-operator NeurIPS 2021

The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space.

Improved architectures and training algorithms for deep operator networks

predictiveintelligencelab/improveddeeponets 4 Oct 2021

In this work we analyze the training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel (NTK) theory, and reveal a bias that favors the approximation of functions with larger magnitudes.

Fast PDE-constrained optimization via self-supervised operator learning

predictiveintelligencelab/pde-constrained-optimization-pi-deeponet 25 Oct 2021

Design and optimal control problems are among the fundamental, ubiquitous tasks we face in science and engineering.

Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers

DarshanDeshpande/research-paper-implementations 24 Nov 2021

AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.