Operator learning
59 papers with code • 0 benchmarks • 1 datasets
Learn an operator between infinite dimensional Hilbert spaces or Banach spaces
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Libraries
Use these libraries to find Operator learning models and implementations
BubbleML
Most implemented papers
Convolutional Analysis Operator Learning: Acceleration and Convergence
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.
Physics-Informed Neural Operator for Learning Partial Differential Equations
devzhk/PINO
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Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution.
Convolutional Analysis Operator Learning: Dependence on Training Data
Convolutional analysis operator learning (CAOL) enables the unsupervised training of (hierarchical) convolutional sparsifying operators or autoencoders from large datasets.
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
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.
GNOT: A General Neural Operator Transformer for Operator Learning
thu-ml/gnot
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However, there are several challenges for learning operators in practical applications like the irregular mesh, multiple input functions, and complexity of the PDEs' solution.
In-Context Operator Learning with Data Prompts for Differential Equation Problems
liuyangmage/in-context-operator-networks
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This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update.
Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning.
An enrichment approach for enhancing the expressivity of neural operators with applications to seismology
The Eikonal equation plays a central role in seismic wave propagation and hypocenter localization, a crucial aspect of efficient earthquake early warning systems.
Importance Weight Estimation and Generalization in Domain Adaptation under Label Shift
We deploy these estimators and provide generalization bounds in the unlabeled target domain.
Learning Symbolic Operators for Task and Motion Planning
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.
