Papers Operator learning
“Operator learning” 태그가 달린 논문 347편 · 필터 해제
FLRONet: Deep Operator Learning for High-Fidelity Fluid Flow Field Reconstruction from Sparse Sensor Measurements
Reconstructing high-fidelity fluid flow fields from sparse sensor measurements is vital for many science and engineering applications but remains challenging because of dimensional disparities between state and observati…
Computational EfficiencyOperator learningSuper-ResolutionAdversarial Autoencoders in Operator Learning
DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various are…
Operator learningSome Best Practices in Operator Learning
Hyperparameters searches are computationally expensive. This paper studies some general choices of hyperparameters and training methods specifically for operator learning. It considers the architectures DeepONets, Fourie…
Operator learningNonlinear Operator Learning Using Energy Minimization and MLPs
We develop and evaluate a method for learning solution operators to nonlinear problems governed by partial differential equations. The approach is based on a finite element discretization and aims at representing the sol…
Operator learningLoss Terms and Operator Forms of Koopman Autoencoders
Koopman autoencoders are a prevalent architecture in operator learning. But, the loss functions and the form of the operator vary significantly in the literature. This paper presents a fair and systemic study of these op…
FormOperator learningPhysics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing meth…
Operator learningOperator learning regularization for macroscopic permeability prediction in dual-scale flow problem
Liquid composites moulding is an important manufacturing technology for fibre reinforced composites, due to its cost-effectiveness. Challenges lie in the optimisation of the process due to the lack of understanding of ke…
Operator learningNeural Operators for Predictor Feedback Control of Nonlinear Delay Systems
Predictor feedback designs are critical for delay-compensating controllers in nonlinear systems. However, these designs are limited in practical applications as predictors cannot be directly implemented, but require nume…
Operator learningDiffeomorphic Latent Neural Operators for Data-Efficient Learning of Solutions to Partial Differential Equations
A computed approximation of the solution operator to a system of partial differential equations (PDEs) is needed in various areas of science and engineering. Neural operators have been shown to be quite effective at pred…
Operator learningVICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
In-Context Operator Networks (ICONs) have demonstrated the ability to learn operators across diverse partial differential equations using few-shot, in-context learning. However, existing ICONs process each spatial point …
Computational EfficiencyIn-Context LearningOperator learningU-WNO:U-Net-enhanced Wavelet Neural Operator for fetal head segmentation
This article describes the development of a novel U-Net-enhanced Wavelet Neural Operator (U-WNO),which combines wavelet decomposition, operator learning, and an encoder-decoder mechanism. This approach harnesses the supe…
Decision MakingDecoderOperator learningSegmentationDeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
We propose a novel fine-tuning method to achieve multi-operator learning through training a distributed neural operator with diverse function data and then zero-shot fine-tuning the neural network using physics-informed …
Operator learningTransfer LearningMitigating Stop-and-Go Traffic Congestion with Operator Learning
This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic …
Computational EfficiencyOperator learningPACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation poses a significant bottleneck, hinderi…
Operator learningThe Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
Operator learning, the approximation of mappings between infinite-dimensional function spaces using machine learning, has gained increasing research attention in recent years. Approximate operators, learned from data, ca…
Operator learningNeural Hamilton: Can A.I. Understand Hamiltonian Mechanics?
We propose a novel framework based on neural network that reformulates classical mechanics as an operator learning problem. A machine directly maps a potential function to its corresponding trajectory in phase space with…
Computational EfficiencyMambaOperator learningLatent Neural Operator Pretraining for Solving Time-Dependent PDEs
Pretraining methods gain increasing attraction recently for solving PDEs with neural operators. It alleviates the data scarcity problem encountered by neural operator learning when solving single PDE via training on larg…
Operator learningOn the Benefits of Active Data Collection in Operator Learning
We investigate active data collection strategies for operator learning when the target operator is linear and the input functions are drawn from a mean-zero stochastic process with continuous covariance kernels. With an …
Operator learningArtificial intelligence for partial differential equations in computational mechanics: A review
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Science: Artificial intelligence for science)…
Operator learningDomain Adaptive Safety Filters via Deep Operator Learning
Learning-based approaches for constructing Control Barrier Functions (CBFs) are increasingly being explored for safety-critical control systems. However, these methods typically require complete retraining when applied t…
Operator learning