Cover Image

Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Huang, Jizu, Qiu, Yue, You, Rukang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Mathematical Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Frequency-adaptive Multi-scale Deep Neural Networks
by: Huang, Jizu, et al.
Published: (2024)

Physics-informed neural networks to solve inverse problems in unbounded domains
by: Pérez-Bernal, Gregorio, et al.
Published: (2025)

Deep learning for the semi-classical limit of the Schrödinger equation
by: Huang, Jizu, et al.
Published: (2025)

A review of graph neural network applications in mechanics-related domains
by: Zhao, Yingxue, et al.
Published: (2024)

Trained quantum neural networks are Gaussian processes
by: Girardi, Filippo, et al.
Published: (2024)

Symbolic identification of tensor equations in multidimensional physical fields
by: Chen, Tianyi, et al.
Published: (2025)

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
by: Zhang, Jingdong, et al.
Published: (2024)

Universal characteristics of deep neural network loss surfaces from random matrix theory
by: Baskerville, Nicholas P, et al.
Published: (2022)

Approximating intractable short ratemodel distribution with neural network
by: Knezevic, Anna, et al.
Published: (2019)

Neumann Series-based Neural Operator for Solving Inverse Medium Problem
by: Liu, Ziyang, et al.
Published: (2024)

Data-driven 2D stationary quantum droplets and wave propagations in the amended GP equation with two potentials via deep neural networks learning
by: Song, Jin, et al.
Published: (2024)

Robustness in sparse artificial neural networks trained with adaptive topology
by: Sulyok, Bendegúz, et al.
Published: (2026)

Spectral integrated neural networks (SINNs) for solving forward and inverse dynamic problems
by: Qiu, Lin, et al.
Published: (2024)

Is the neural tangent kernel of PINNs deep learning general partial differential equations always convergent ?
by: Zhou, Zijian, et al.
Published: (2024)

Dynamic graph neural networks for enhanced volatility prediction in financial markets
by: Kumar, Pulikandala Nithish, et al.
Published: (2024)

Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime
by: Camilli, Francesco, et al.
Published: (2025)

The cross-sectional stock return predictions via quantum neural network and tensor network
by: Kobayashi, Nozomu, et al.
Published: (2023)

Seeding neural network quantum states with tensor network states
by: Kaneko, Ryui, et al.
Published: (2025)

Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing
by: Basterrech, Sebastian, et al.
Published: (2025)

Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)

A Learning-based Domain Decomposition Method
by: Wu, Rui, et al.
Published: (2025)

RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles
by: Behnoudfar, Pouria, et al.
Published: (2025)

DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling
by: Zang, Yaohua, et al.
Published: (2025)

PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
by: Wu, Junyi, et al.
Published: (2025)

FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
by: Menon, Sidharth S., et al.
Published: (2026)

Physics-Informed Deep Learning of Rate-and-State Fault Friction
by: Rucker, Cody, et al.
Published: (2023)

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments
by: Mishra, Aashwin, et al.
Published: (2026)

Learning Heat-based Equations in Self-similar variables
by: Wang, Shihao, et al.
Published: (2026)

Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)

BEACON: Bayesian Experimental design Acceleration with Conditional Normalizing flows $-$ a case study in optimal monitor well placement for CO$_2$ sequestration
by: Orozco, Rafael, et al.
Published: (2024)

Loss Jump During Loss Switch in Solving PDEs with Neural Networks
by: Wang, Zhiwei, et al.
Published: (2024)

Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)

Generative Path-Finding Method for Wasserstein Gradient Flow
by: Liu, Chengyu, et al.
Published: (2026)

Symbolic Recovery of Differential Equations: The Identifiability Problem
by: Scholl, Philipp, et al.
Published: (2022)

ASPINN: An asymptotic strategy for solving singularly perturbed differential equations
by: Wang, Sen, et al.
Published: (2024)

A DNN Biophysics Model with Topological and Electrostatic Features
by: Sliheet, Elyssa, et al.
Published: (2024)

Continual Learning as a Multiphase Moving-Boundary Problem
by: Khilar, Snigdha Chandan
Published: (2026)

Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
by: Geng, Ru, et al.
Published: (2026)

Self-adaptive weighting and sampling for physics-informed neural networks
by: Chen, Wenqian, et al.
Published: (2025)

Implementation of tangent linear and adjoint models for neural networks based on a compiler library tool
by: Xiao, Sa, et al.
Published: (2026)