:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Benavides-Herrera, Pablo, Ruiz-Cruz, Riemann, Sánchez-Torres, Juan Diego
Format:	Preprint
Published:	2026
Subjects:	Numerical Analysis Machine Learning
Online Access:	https://arxiv.org/abs/2605.01446
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Energy Approach from $\varepsilon$-Graph to Continuum Diffusion Model with Connectivity Functional
by: Yang, Yahong, et al.
Published: (2025)

Nonlinear Assimilation via Score-based Sequential Langevin Sampling
by: Ding, Zhao, et al.
Published: (2024)

Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
by: Sharma, Harsh, et al.
Published: (2025)

Nonlinear energy-preserving model reduction with lifting transformations that quadratize the energy
by: Sharma, Harsh, et al.
Published: (2025)

End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver
by: Ma, Shaocong, et al.
Published: (2024)

Alternatives to the Laplacian for Scalable Spectral Clustering with Group Fairness Constraints
by: Ojeda-Ruiz, Iván, et al.
Published: (2025)

KKANs: Kurkova-Kolmogorov-Arnold Networks and Their Learning Dynamics
by: Toscano, Juan Diego, et al.
Published: (2024)

Sequential decoder training for improved latent space dynamics identification
by: Anderson, William, et al.
Published: (2025)

Sub-Sequential Physics-Informed Learning with State Space Model
by: Xu, Chenhui, et al.
Published: (2025)

Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)

Matrix Completion with Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling
by: Cai, HanQin, et al.
Published: (2022)

Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
by: Cheng, Xiaoran, et al.
Published: (2024)

Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)

Revisiting Orbital Minimization Method for Neural Operator Decomposition
by: Ryu, J. Jon, et al.
Published: (2025)

Random test functions, $H^{-1}$ norm equivalence, and stochastic variational physics-informed neural networks
by: Marcondes, Diego
Published: (2026)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
by: Zhang, Huan, et al.
Published: (2024)

Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
by: Fang, Yuchen, et al.
Published: (2024)

Cons-training Tensor Networks: Embedding and Optimization Over Discrete Linear Constraints
by: Lopez-Piqueres, Javier, et al.
Published: (2024)

Persistent-Transient Policy Evaluation for Markov Chains via Minimal Peripheral Quotients
by: Xu, Yang, et al.
Published: (2026)

Determinant Estimation under Memory Constraints and Neural Scaling Laws
by: Ameli, Siavash, et al.
Published: (2025)

Latent Neural Operator Pretraining for Solving Time-Dependent PDEs
by: Wang, Tian, et al.
Published: (2024)

A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2023)

Extending Path-Dependent NJ-ODEs to Noisy Observations and a Dependent Observation Framework
by: Andersson, William, et al.
Published: (2023)

Step-Size Decay and Structural Stagnation in Greedy Sparse Learning
by: Berná, Pablo M.
Published: (2026)

TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
by: Dai, Chen-Yang, et al.
Published: (2026)

Convergence and Sketching-Based Efficient Computation of Neural Tangent Kernel Weights in Physics-Based Loss
by: Hirsch, Max, et al.
Published: (2025)

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
by: O'Leary-Roseberry, Thomas, et al.
Published: (2024)

On the Convergence of Irregular Sampling in Reproducing Kernel Hilbert Spaces
by: Iske, Armin
Published: (2025)

Decoupled Diffusion Sampling for Inverse Problems on Function Spaces
by: Lin, Thomas Y. L., et al.
Published: (2026)

Sample Efficient Learning of Factored Embeddings of Tensor Fields
by: Heo, Taemin, et al.
Published: (2022)

ADMM for Structured Fractional Minimization
by: Yuan, Ganzhao
Published: (2024)

Exact Constraint Enforcement in Physics-Informed Extreme Learning Machines using Null-Space Projection Framework
by: Mishra, Rishi, et al.
Published: (2026)

The Sample Complexity of Learning Lipschitz Operators with respect to Gaussian Measures
by: Adcock, Ben, et al.
Published: (2024)

Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference
by: Chuang, Pi-Yueh, et al.
Published: (2025)

Guaranteed Sampling Flexibility for Low-tubal-rank Tensor Completion
by: Su, Bowen, et al.
Published: (2024)

Tensor Completion with Nearly Linear Samples Given Weak Side Information
by: Yu, Christina Lee, et al.
Published: (2020)

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
by: Tang, Kejun, et al.
Published: (2023)

The Geometry of the Pivot: A Note on Lazy Pivoted Cholesky and Farthest Point Sampling
by: Shabat, Gil
Published: (2026)

Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance
by: Chen, Yifan, et al.
Published: (2023)

RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
by: Song, Zhenao
Published: (2025)