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Operator learning
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Learn an operator between infinite dimensional Hilbert spaces or Banach spaces
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
OmniFluids: Unified Physics Pre-trained Modeling of Fluid Dynamics
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
Mondrian: Transformer Operators via Domain Decomposition
PMNO: A novel physics guided multi-step neural operator predictor for partial differential equations
Recurrent Neural Operators: Stable Long-Term PDE Prediction
Learning Where to Learn: Training Distribution Selection for Provable OOD Performance
Graph-Based Operator Learning from Limited Data on Irregular Domains
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
Multi-Level Monte Carlo Training of Neural Operators
Neural Functional: Learning Function to Scalar Maps for Neural PDE Surrogates
Beyond Accuracy: EcoL2 Metric for Sustainable Neural PDE Solvers
Learning cardiac activation and repolarization times with operator learning
Physics-informed Multiple-Input Operators for efficient dynamic response prediction of structures
SetONet: A Deep Set-based Operator Network for Solving PDEs with permutation invariant variable input sampling
Data-driven operator learning for energy-efficient building control
A Hybrid Framework for Efficient Koopman Operator Learning
Geometry aware inference of steady state PDEs using Equivariant Neural Fields representations
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
Operator Learning: A Statistical Perspective
DeepOHeat-v1: Efficient Operator Learning for Fast and Trustworthy Thermal Simulation and Optimization in 3D-IC Design
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary Geometries
Inverted Gaussian Process Optimization for Nonparametric Koopman Operator Discovery
Towards scientific machine learning for granular material simulations -- challenges and opportunities
New universal operator approximation theorem for encoder-decoder architectures (Preprint)
DeepOFormer: Deep Operator Learning with Domain-informed Features for Fatigue Life Prediction
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
Data-Driven, ML-assisted Approaches to Problem Well-Posedness
Invertible Koopman neural operator for data-driven modeling of partial differential equations
Theory-to-Practice Gap for Neural Networks and Neural Operators
Physics-Informed Deep B-Spline Networks for Dynamical Systems
ON-Traffic: An Operator Learning Framework for Online Traffic Flow Estimation and Uncertainty Quantification from Lagrangian Sensors
Active operator learning with predictive uncertainty quantification for partial differential equations
Improve Representation for Imbalanced Regression through Geometric Constraints
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.