:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Liang, Zhangyong, Zhang, Ji
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	https://arxiv.org/abs/2603.13751
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Disentangled Latent Dynamics Manifold Fusion for Solving Parameterized PDEs
por: Liang, Zhangyong
Publicado: (2026)

Stochastic-Dimension Frozen Sampled Neural Network for High-Dimensional Gross-Pitaevskii Equations on Unbounded Domains
por: Liang, Zhangyong
Publicado: (2026)

Hybrid Iterative Neural Low-Regularity Integrator for Nonlinear Dispersive Equations
por: Liang, Zhangyong
Publicado: (2026)

Conflict-Aware Harmonized Rotational Gradient for Multiscale Kinetic Regimes
por: Liang, Zhangyong
Publicado: (2026)

Error Estimate and Convergence Analysis for Data Valuation
por: Liang, Zhangyong, et al.
Publicado: (2025)

Neural Dynamic Data Valuation: A Stochastic Optimal Control Approach
por: Liang, Zhangyong, et al.
Publicado: (2024)

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

Function Extrapolation with Neural Networks and Its Application for Manifolds
por: Hay, Guy, et al.
Publicado: (2024)

Stochastic Dimension-Free Zeroth-Order Estimator for High-Dimensional and High-Order PINNs
por: Liang, Zhangyong, et al.
Publicado: (2026)

Categorical Optimization with Bayesian Anchored Latent Trust Regions for Structural Design under High-Dimensional Uncertainty
por: Liang, Zhangyong, et al.
Publicado: (2026)

Constitutive parameterized deep energy method for solid mechanics problems with random material parameters
por: Liang, Zhangyong, et al.
Publicado: (2026)

Parameterized Physics-informed Neural Networks for Parameterized PDEs
por: Cho, Woojin, et al.
Publicado: (2024)

Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
por: Ko, Gyeonghoon, et al.
Publicado: (2026)

Dual-Balancing for Physics-Informed Neural Networks
por: Zhou, Chenhong, et al.
Publicado: (2025)

Deep Neural Networks Tend To Extrapolate Predictably
por: Kang, Katie, et al.
Publicado: (2023)

Prior-Informed Neural Network Initialization: A Spectral Approach for Function Parameterizing Architectures
por: Torres, David Orlando Salazar, et al.
Publicado: (2026)

Weak Physics Informed Neural Networks for Geometry Compatible Hyperbolic Conservation Laws on Manifolds
por: Zhou, Hanfei, et al.
Publicado: (2025)

Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization
por: Li, Rui, et al.
Publicado: (2024)

A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
por: Shin, Jeesuk, et al.
Publicado: (2026)

Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference
por: Shih, Frank, et al.
Publicado: (2025)

Stability to Deformations of Manifold Filters and Manifold Neural Networks
por: Wang, Zhiyang, et al.
Publicado: (2021)

Matrix Manifold Neural Networks++
por: Nguyen, Xuan Son, et al.
Publicado: (2024)

Why Cannot Neural Networks Master Extrapolation? Insights from Physical Laws
por: Dakhmouche, Ramzi, et al.
Publicado: (2025)

Dual Natural Gradient Descent for Scalable Training of Physics-Informed Neural Networks
por: Jnini, Anas, et al.
Publicado: (2025)

Conformalized Physics-Informed Neural Networks
por: Podina, Lena, et al.
Publicado: (2024)

Model Based and Physics Informed Deep Learning Neural Network Structures
por: Mohammad-Djafari, Ali, et al.
Publicado: (2024)

Physics-Informed Neural Networks and Neural Operators for Parametric PDEs
por: Zhang, Zhuo, et al.
Publicado: (2025)

Function-space Parameterization of Neural Networks for Sequential Learning
por: Scannell, Aidan, et al.
Publicado: (2024)

DeepONet as a Multi-Operator Extrapolation Model: Distributed Pretraining with Physics-Informed Fine-Tuning
por: Zhang, Zecheng, et al.
Publicado: (2024)

Forget by Uncertainty: Orthogonal Entropy Unlearning for Quantized Neural Networks
por: Zhang, Tian, et al.
Publicado: (2026)

Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
por: Hwang, Youngsik, et al.
Publicado: (2024)

Exploring the Manifold of Neural Networks Using Diffusion Geometry
por: Abel, Elliott, et al.
Publicado: (2024)

Guaranteeing Conservation of Integrals with Projection in Physics-Informed Neural Networks
por: Baez, Anthony, et al.
Publicado: (2025)

Physics-Informed Spiking Neural Networks via Conservative Flux Quantization
por: Zhang, Chi, et al.
Publicado: (2025)

Preconditioning for Physics-Informed Neural Networks
por: Liu, Songming, et al.
Publicado: (2024)

Complex Physics-Informed Neural Network
por: Si, Chenhao, et al.
Publicado: (2025)

Physics-Informed Neural Networks and Extensions
por: Raissi, Maziar, et al.
Publicado: (2024)

Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
por: Baez, Anthony, et al.
Publicado: (2024)

Unsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
por: Liang, Yunhui, et al.
Publicado: (2024)

Perception-Informed Neural Networks: Beyond Physics-Informed Neural Networks
por: Mazandarani, Mehran, et al.
Publicado: (2025)