Saved in:
| Main Author: | Neugebauer, Marcel |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.07277 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Samplet limits and multiwavelets
by: Giacchi, Gianluca, et al.
Published: (2026)
by: Giacchi, Gianluca, et al.
Published: (2026)
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
by: Baroli, Davide, et al.
Published: (2023)
by: Baroli, Davide, et al.
Published: (2023)
Samplets: Wavelet concepts for scattered data
by: Harbrecht, Helmut, et al.
Published: (2025)
by: Harbrecht, Helmut, et al.
Published: (2025)
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
by: Kim, Hwanwoo, et al.
Published: (2024)
by: Kim, Hwanwoo, et al.
Published: (2024)
Calibrated Computation-Aware Gaussian Processes
by: Hegde, Disha, et al.
Published: (2024)
by: Hegde, Disha, et al.
Published: (2024)
Bayesian Quadrature: Gaussian Processes for Integration
by: Mahsereci, Maren, et al.
Published: (2026)
by: Mahsereci, Maren, et al.
Published: (2026)
Preconditioned Additive Gaussian Processes with Fourier Acceleration
by: Wagner, Theresa, et al.
Published: (2025)
by: Wagner, Theresa, et al.
Published: (2025)
Sketching the Heat Kernel: Using Gaussian Processes to Embed Data
by: Gilbert, Anna C., et al.
Published: (2024)
by: Gilbert, Anna C., et al.
Published: (2024)
Mode-Shape Expansion Using Physics-Constrained Gaussian Process Regression
by: Ghahari, Farid
Published: (2026)
by: Ghahari, Farid
Published: (2026)
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers
by: Pförtner, Marvin, et al.
Published: (2022)
by: Pförtner, Marvin, et al.
Published: (2022)
Low-rank computation of the posterior mean in Multi-Output Gaussian Processes
by: Esche, Sebastian, et al.
Published: (2025)
by: Esche, Sebastian, et al.
Published: (2025)
Posterior Covariance Structures in Gaussian Processes
by: Cai, Difeng, et al.
Published: (2024)
by: Cai, Difeng, et al.
Published: (2024)
A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
by: Mora, Carlos, et al.
Published: (2024)
by: Mora, Carlos, et al.
Published: (2024)
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
by: Li, Zhaohui, et al.
Published: (2022)
by: Li, Zhaohui, et al.
Published: (2022)
Gaussian Process Regression under Computational and Epistemic Misspecification
by: Sanz-Alonso, Daniel, et al.
Published: (2023)
by: Sanz-Alonso, Daniel, et al.
Published: (2023)
Gaussian Processes and Reproducing Kernels: Connections and Equivalences
by: Kanagawa, Motonobu, et al.
Published: (2025)
by: Kanagawa, Motonobu, et al.
Published: (2025)
From Simple to Complex: Curriculum-Guided Physics-Informed Neural Networks via Gaussian Mixture Models
by: Yang, Jianan, et al.
Published: (2026)
by: Yang, Jianan, et al.
Published: (2026)
ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units via Chebyshev Approximations
by: Tang, Shanshan, et al.
Published: (2019)
by: Tang, Shanshan, et al.
Published: (2019)
Construction of generalized samplets in Banach spaces
by: Balazs, Peter, et al.
Published: (2024)
by: Balazs, Peter, et al.
Published: (2024)
Slicing the Gaussian Mixture Wasserstein Distance
by: Piening, Moritz, et al.
Published: (2025)
by: Piening, Moritz, et al.
Published: (2025)
Numerical Considerations for the Construction of Karhunen-Loève Expansions
by: Safta, Cosmin, et al.
Published: (2026)
by: Safta, Cosmin, et al.
Published: (2026)
A Data-Driven Interpolation Method on Smooth Manifolds via Diffusion Processes and Voronoi Tessellations
by: Gomez, Alvaro Almeida
Published: (2025)
by: Gomez, Alvaro Almeida
Published: (2025)
A Discrete Perspective Towards the Construction of Sparse Probabilistic Boolean Networks
by: Fok, Christopher H., et al.
Published: (2024)
by: Fok, Christopher H., et al.
Published: (2024)
The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Data-driven Learning of Interaction Laws in Multispecies Particle Systems with Gaussian Processes: Convergence Theory and Applications
by: Feng, Jinchao, et al.
Published: (2025)
by: Feng, Jinchao, et al.
Published: (2025)
Wasserstein Bounds for generative diffusion models with Gaussian tail targets
by: Wang, Xixian, et al.
Published: (2024)
by: Wang, Xixian, et al.
Published: (2024)
Weak Form Scientific Machine Learning: Test Function Construction for System Identification
by: Tran, April, et al.
Published: (2025)
by: Tran, April, et al.
Published: (2025)
Sparse discovery of differential equations based on multi-fidelity Gaussian process
by: Meng, Yuhuang, et al.
Published: (2024)
by: Meng, Yuhuang, et al.
Published: (2024)
Improving the Predictability of the Madden-Julian Oscillation at Subseasonal Scales with Gaussian Process Models
by: Chen, Haoyuan, et al.
Published: (2025)
by: Chen, Haoyuan, et al.
Published: (2025)
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)
Solving All Regression Models For Learning Gaussian Networks Using Givens Rotations
by: Alipourfard, Borzou, et al.
Published: (2019)
by: Alipourfard, Borzou, et al.
Published: (2019)
Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
by: Chen, Yifan, et al.
Published: (2023)
by: Chen, Yifan, et al.
Published: (2023)
Stable Derivative Free Gaussian Mixture Variational Inference for Bayesian Inverse Problems
by: Che, Baojun, et al.
Published: (2025)
by: Che, Baojun, et al.
Published: (2025)
Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields
by: Weiland, Tim, et al.
Published: (2025)
by: Weiland, Tim, et al.
Published: (2025)
Exact Gaussian Moment Matching for Residual Networks: a Second-Order Method
by: Kuang, Simon, et al.
Published: (2026)
by: Kuang, Simon, et al.
Published: (2026)
Fourier Neural Operators for Non-Markovian Processes:Approximation Theorems and Experiments
by: Lee, Wonjae, et al.
Published: (2025)
by: Lee, Wonjae, et al.
Published: (2025)
In Situ Quantum Analog Pulse Characterization via Structured Signal Processing
by: Dong, Yulong, et al.
Published: (2025)
by: Dong, Yulong, et al.
Published: (2025)
Learning reduced-order Quadratic-Linear models in Process Engineering using Operator Inference
by: Gosea, Ion Victor, et al.
Published: (2024)
by: Gosea, Ion Victor, et al.
Published: (2024)
GPLaSDI: Gaussian Process-based Interpretable Latent Space Dynamics Identification through Deep Autoencoder
by: Bonneville, Christophe, et al.
Published: (2023)
by: Bonneville, Christophe, et al.
Published: (2023)
Multidimensional unstructured sparse recovery via eigenmatrix
by: Ying, Lexing
Published: (2024)
by: Ying, Lexing
Published: (2024)
Similar Items
-
Samplet limits and multiwavelets
by: Giacchi, Gianluca, et al.
Published: (2026) -
Samplet basis pursuit: Multiresolution scattered data approximation with sparsity constraints
by: Baroli, Davide, et al.
Published: (2023) -
Samplets: Wavelet concepts for scattered data
by: Harbrecht, Helmut, et al.
Published: (2025) -
Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration
by: Kim, Hwanwoo, et al.
Published: (2024) -
Calibrated Computation-Aware Gaussian Processes
by: Hegde, Disha, et al.
Published: (2024)