Copertina

Integral Operator Approaches for Scattered Data Fitting on Spheres

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Lin, Shao-Bo
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Numerical Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Universal Approximation of Operators with Transformers and Neural Integral Operators
di: Zappala, Emanuele, et al.
Pubblicazione: (2024)

Reduced-Basis Deep Operator Learning for Parametric PDEs with Independently Varying Boundary and Source Data
di: Wang, Yueqi, et al.
Pubblicazione: (2025)

Neural Operators with Localized Integral and Differential Kernels
di: Liu-Schiaffini, Miguel, et al.
Pubblicazione: (2024)

Solving Poisson Equations using Neural Walk-on-Spheres
di: Nam, Hong Chul, et al.
Pubblicazione: (2024)

Condition Numbers and Eigenvalue Spectra of Shallow Networks on Spheres
di: Liu, Xinliang, et al.
Pubblicazione: (2025)

Data Complexity Estimates for Operator Learning
di: Kovachki, Nikola B., et al.
Pubblicazione: (2024)

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
di: Ling, Shuo, et al.
Pubblicazione: (2024)

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere
di: Mao, Tong, et al.
Pubblicazione: (2025)

Integral regularization PINNs for evolution equations
di: Feng, Xiaodong, et al.
Pubblicazione: (2025)

Multi-scale DeepOnet (Mscale-DeepOnet) for Mitigating Spectral Bias in Learning High Frequency Operators of Oscillatory Functions
di: Wang, Bo, et al.
Pubblicazione: (2025)

Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics
di: Santos, Rômulo Damasclin Chaves dos, et al.
Pubblicazione: (2025)

Stabilizing and Solving Unique Continuation Problems by Parameterizing Data and Learning Finite Element Solution Operators
di: Burman, Erik, et al.
Pubblicazione: (2024)

DeltaPhi: Physical States Residual Learning for Neural Operators in Data-Limited PDE Solving
di: Yue, Xihang, et al.
Pubblicazione: (2024)

Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
di: Jalalian, Yasamin, et al.
Pubblicazione: (2025)

Deep Neural Network Solutions for Oscillatory Fredholm Integral Equations
di: Jiang, Jie, et al.
Pubblicazione: (2024)

Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective
di: Lanthaler, Samuel
Pubblicazione: (2024)

MODNO: Multi Operator Learning With Distributed Neural Operators
di: Zhang, Zecheng
Pubblicazione: (2024)

Data-Driven, ML-assisted Approaches to Problem Well-Posedness
di: Bertalan, Tom, et al.
Pubblicazione: (2025)

Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
di: Moya, Christian, et al.
Pubblicazione: (2024)

Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
di: Zhang, Borong, et al.
Pubblicazione: (2024)

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
di: Zhang, Borong, et al.
Pubblicazione: (2022)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
di: Hardwick, Gareth, et al.
Pubblicazione: (2024)

UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning
di: Qiao, Hanli, et al.
Pubblicazione: (2026)

Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series KMeans Clustering and Multi-Criteria Decision Making
di: Howard, Daniel Anthony, et al.
Pubblicazione: (2024)

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning
di: Lowery, Matthew, et al.
Pubblicazione: (2024)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
di: Liang, Zhangyong
Pubblicazione: (2026)

Continuum Attention for Neural Operators
di: Calvello, Edoardo, et al.
Pubblicazione: (2024)

Operator Learning: Algorithms and Analysis
di: Kovachki, Nikola B., et al.
Pubblicazione: (2024)

The Parametric Complexity of Operator Learning
di: Lanthaler, Samuel, et al.
Pubblicazione: (2023)

Operator Learning at Machine Precision
di: Bacho, Aras, et al.
Pubblicazione: (2025)

fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
di: Lu, Binghang, et al.
Pubblicazione: (2026)

A Boundary Integral-based Neural Operator for Mesh Deformation
di: Wu, Zhengyu, et al.
Pubblicazione: (2026)

Data-driven Model Reduction for Soft Robots via Lagrangian Operator Inference
di: Sharma, Harsh, et al.
Pubblicazione: (2024)

Leray-Schauder Mappings for Operator Learning
di: Zappala, Emanuele
Pubblicazione: (2024)

SpectraKAN: Conditioning Spectral Operators
di: Cheng, Chun-Wun, et al.
Pubblicazione: (2026)

Spectral Audit of In-Context Operator Networks
di: Gao, Zhiwei, et al.
Pubblicazione: (2026)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
di: Lei, Guanhang, et al.
Pubblicazione: (2023)

Physics-Informed Geometry-Aware Neural Operator
di: Zhong, Weiheng, et al.
Pubblicazione: (2024)

A Mathematical Analysis of Neural Operator Behaviors
di: Le, Vu-Anh, et al.
Pubblicazione: (2024)

Nonlocality and Nonlinearity Implies Universality in Operator Learning
di: Lanthaler, Samuel, et al.
Pubblicazione: (2023)