Operator learning
59 papers with code • 0 benchmarks • 1 datasets
Learn an operator between infinite dimensional Hilbert spaces or Banach spaces
Benchmarks
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Libraries
Use these libraries to find Operator learning models and implementations
BubbleML
Most implemented papers
A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues from Digital Image Correlation Measurements
To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge.
Deep transfer operator learning for partial differential equations under conditional shift
Transfer learning (TL) enables the transfer of knowledge gained in learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labeling, potential computational power limitations, and dataset distribution mismatches.
U-NO: U-shaped Neural Operators
ashiq24/uno
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We show that U-NO results in an average of 26% and 44% prediction improvement on Darcy's flow and turbulent Navier-Stokes equations, respectively, over the state of the art.
Transformer for Partial Differential Equations' Operator Learning
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions.
Learning Efficient Abstract Planning Models that Choose What to Predict
An effective approach to solving long-horizon tasks in robotics domains with continuous state and action spaces is bilevel planning, wherein a high-level search over an abstraction of an environment is used to guide low-level decision-making.
Learning dynamical systems: an example from open quantum system dynamics
Machine learning algorithms designed to learn dynamical systems from data can be used to forecast, control and interpret the observed dynamics.
Fast Sampling of Diffusion Models via Operator Learning
Diffusion models have found widespread adoption in various areas.
Transform Once: Efficient Operator Learning in Frequency Domain
diffeqml/kairos
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Instead, this work introduces a blueprint for frequency domain learning through a single transform: transform once (T1).
Guiding continuous operator learning through Physics-based boundary constraints
Numerical experiments based on multiple PDEs with a wide variety of applications indicate that the proposed approach ensures satisfaction of BCs, and leads to more accurate solutions over the entire domain.
Convolutional Neural Operators for robust and accurate learning of PDEs
bogdanraonic3/convolutionalneuraloperator
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NeurIPS 2023
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs.
