Data-intrinsic approximation in metric spaces
Fuente:
arXiv
Saved in:
| Main Authors: | Dölz, Jürgen, Multerer, Michael |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Construction of generalized samplets in Banach spaces
by: Balazs, Peter, et al.
Published: (2024)
by: Balazs, Peter, et al.
Published: (2024)
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
by: Baroli, Davide, et al.
Published: (2023)
by: Baroli, Davide, et al.
Published: (2023)
Observation-specific explanations through scattered data approximation
by: Ghidini, Valentina, et al.
Published: (2024)
by: Ghidini, Valentina, et al.
Published: (2024)
On Quasi-Localized Dual Pairs in Reproducing Kernel Hilbert Spaces
by: Harbrecht, Helmut, et al.
Published: (2024)
by: Harbrecht, Helmut, et al.
Published: (2024)
Multiscale scattered data analysis in samplet coordinates
by: Avesani, Sara, et al.
Published: (2024)
by: Avesani, Sara, et al.
Published: (2024)
Samplet limits and multiwavelets
by: Giacchi, Gianluca, et al.
Published: (2026)
by: Giacchi, Gianluca, et al.
Published: (2026)
Multiresolution local smoothness detection in non-uniformly sampled multivariate signals
by: Avesani, Sara, et al.
Published: (2025)
by: Avesani, Sara, et al.
Published: (2025)
The dimension weighted fast multipole method for scattered data approximation
by: Harbrecht, Helmut, et al.
Published: (2024)
by: Harbrecht, Helmut, et al.
Published: (2024)
Weighted variation spaces and approximation by shallow ReLU networks
by: DeVore, Ronald, et al.
Published: (2023)
by: DeVore, Ronald, et al.
Published: (2023)
Fully discrete analysis of the Galerkin POD neural network approximation with application to 3D acoustic wave scattering
by: Dölz, Jürgen, et al.
Published: (2025)
by: Dölz, Jürgen, et al.
Published: (2025)
Beyond Lipschitz: Data-Driven Robustness via Discrete Modulus of Continuity
by: Dölz, Jürgen, et al.
Published: (2026)
by: Dölz, Jürgen, et al.
Published: (2026)
Samplets: Wavelet concepts for scattered data
by: Harbrecht, Helmut, et al.
Published: (2025)
by: Harbrecht, Helmut, et al.
Published: (2025)
Adaptive joint distribution learning
by: Filipovic, Damir, et al.
Published: (2021)
by: Filipovic, Damir, et al.
Published: (2021)
Optimal sampling for least-squares approximation
by: Adcock, Ben
Published: (2024)
by: Adcock, Ben
Published: (2024)
Wasserstein approximation schemes based on Voronoi partitions
by: Hamm, Keaton, et al.
Published: (2023)
by: Hamm, Keaton, et al.
Published: (2023)
Exact and approximate error bounds for physics-informed neural networks
by: Chantada, Augusto T., et al.
Published: (2024)
by: Chantada, Augusto T., et al.
Published: (2024)
Physics-Informed DeepONets for drift-diffusion on metric graphs: simulation and parameter identification
by: Blechschmidt, Jan, et al.
Published: (2025)
by: Blechschmidt, Jan, et al.
Published: (2025)
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
by: Luo, Dingcheng, et al.
Published: (2025)
by: Luo, Dingcheng, et al.
Published: (2025)
A unified error analysis for randomized low-rank approximation with application to data assimilation
by: Di Perrotolo, Alexandre Scotto, et al.
Published: (2024)
by: Di Perrotolo, Alexandre Scotto, et al.
Published: (2024)
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
by: Yang, Yunfei
Published: (2024)
by: Yang, Yunfei
Published: (2024)
When big data actually are low-rank, or entrywise approximation of certain function-generated matrices
by: Budzinskiy, Stanislav
Published: (2024)
by: Budzinskiy, Stanislav
Published: (2024)
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning
by: Bistrian, Diana Alina, et al.
Published: (2022)
by: Bistrian, Diana Alina, et al.
Published: (2022)
An intrinsic expansion approach to the Galerkin approximations for the Navier-Stokes equations
by: Hoang, Luan, et al.
Published: (2026)
by: Hoang, Luan, et al.
Published: (2026)
Sequential decoder training for improved latent space dynamics identification
by: Anderson, William, et al.
Published: (2025)
by: Anderson, William, et al.
Published: (2025)
Optimal deep learning of holomorphic operators between Banach spaces
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
by: Ivagnes, Anna, et al.
Published: (2022)
by: Ivagnes, Anna, et al.
Published: (2022)
mLaSDI: Multi-stage latent space dynamics identification
by: Anderson, William, et al.
Published: (2025)
by: Anderson, William, et al.
Published: (2025)
A local approach to parameter space reduction for regression and classification tasks
by: Romor, Francesco, et al.
Published: (2021)
by: Romor, Francesco, et al.
Published: (2021)
Local sensitivity analysis for Bayesian inverse problems
by: Dölz, Jürgen, et al.
Published: (2025)
by: Dölz, Jürgen, et al.
Published: (2025)
On uncertainty quantification of eigenvalues and eigenspaces with higher multiplicity
by: Dölz, Jürgen, et al.
Published: (2022)
by: Dölz, Jürgen, et al.
Published: (2022)
Parametric Shape Holomorphy of Boundary Integral Operators with Applications
by: Dölz, Jürgen, et al.
Published: (2023)
by: Dölz, Jürgen, et al.
Published: (2023)
Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)
by: Beck, Christian, et al.
Published: (2020)
Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
by: Zhuang, Bozhou, et al.
Published: (2024)
by: Zhuang, Bozhou, et al.
Published: (2024)
On Uniform Weighted Deep Polynomial approximation
by: Yeon, Kingsley, et al.
Published: (2025)
by: Yeon, Kingsley, et al.
Published: (2025)
Data-driven approximation of transfer operators for mean-field stochastic differential equations
by: Ioannou, Eirini, et al.
Published: (2025)
by: Ioannou, Eirini, et al.
Published: (2025)
Correction to "Wasserstein distance estimates for the distributions of numerical approximations to ergodic stochastic differential equations"
by: Paulin, Daniel, et al.
Published: (2024)
by: Paulin, Daniel, et al.
Published: (2024)
Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense
by: Neufeld, Ariel, et al.
Published: (2024)
by: Neufeld, Ariel, et al.
Published: (2024)
Geometrical structures of digital fluctuations in parameter space of neural networks trained with adaptive momentum optimization
by: Netay, Igor V.
Published: (2024)
by: Netay, Igor V.
Published: (2024)
Large Data Limits of Laplace Learning for Gaussian Measure Data in Infinite Dimensions
by: Zhong, Zhengang, et al.
Published: (2026)
by: Zhong, Zhengang, et al.
Published: (2026)
Space-time deep neural network approximations for high-dimensional partial differential equations
by: Hornung, Fabian, et al.
Published: (2020)
by: Hornung, Fabian, et al.
Published: (2020)
Similar Items
-
Construction of generalized samplets in Banach spaces
by: Balazs, Peter, et al.
Published: (2024) -
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
by: Baroli, Davide, et al.
Published: (2023) -
Observation-specific explanations through scattered data approximation
by: Ghidini, Valentina, et al.
Published: (2024) -
On Quasi-Localized Dual Pairs in Reproducing Kernel Hilbert Spaces
by: Harbrecht, Helmut, et al.
Published: (2024) -
Multiscale scattered data analysis in samplet coordinates
by: Avesani, Sara, et al.
Published: (2024)