Saved in:
| Main Authors: | Wang, Zhiwei, Zhang, Lulu, Zhang, Zhongwang, Xu, Zhi-Qin John |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.03095 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Loss Spike in Training Neural Networks
by: Li, Xiaolong, et al.
Published: (2023)
by: Li, Xiaolong, et al.
Published: (2023)
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks
by: Bai, Zhiwei, et al.
Published: (2022)
by: Bai, Zhiwei, et al.
Published: (2022)
The Loss Surfaces of Neural Networks with General Activation Functions
by: Baskerville, Nicholas P., et al.
Published: (2020)
by: Baskerville, Nicholas P., et al.
Published: (2020)
Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing
by: Zhang, Zhongwang, et al.
Published: (2024)
by: Zhang, Zhongwang, et al.
Published: (2024)
Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
by: Yin, Pengsong, et al.
Published: (2024)
by: Yin, Pengsong, et al.
Published: (2024)
Local Linear Recovery Guarantee of Deep Neural Networks at Overparameterization
by: Zhang, Yaoyu, et al.
Published: (2024)
by: Zhang, Yaoyu, et al.
Published: (2024)
Complexity Control Facilitates Reasoning-Based Compositional Generalization in Transformers
by: Zhang, Zhongwang, et al.
Published: (2025)
by: Zhang, Zhongwang, et al.
Published: (2025)
Neumann Series-based Neural Operator for Solving Inverse Medium Problem
by: Liu, Ziyang, et al.
Published: (2024)
by: Liu, Ziyang, et al.
Published: (2024)
Efficient High-Accuracy PDEs Solver with the Linear Attention Neural Operator
by: Zhong, Ming, et al.
Published: (2025)
by: Zhong, Ming, et al.
Published: (2025)
Reasoning Bias of Next Token Prediction Training
by: Lin, Pengxiao, et al.
Published: (2025)
by: Lin, Pengxiao, et al.
Published: (2025)
An Analysis for Reasoning Bias of Language Models with Small Initialization
by: Yao, Junjie, et al.
Published: (2025)
by: Yao, Junjie, 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)
by: Zang, Yaohua, et al.
Published: (2025)
Symbolic Neural Ordinary Differential Equations
by: Li, Xin, et al.
Published: (2025)
by: Li, Xin, et al.
Published: (2025)
Efficient Error Certification for Physics-Informed Neural Networks
by: Eiras, Francisco, et al.
Published: (2023)
by: Eiras, Francisco, et al.
Published: (2023)
UGrid: An Efficient-And-Rigorous Neural Multigrid Solver for Linear PDEs
by: Han, Xi, et al.
Published: (2024)
by: Han, Xi, et al.
Published: (2024)
Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
by: Hoshisashi, Kentaro, et al.
Published: (2026)
by: Hoshisashi, Kentaro, et al.
Published: (2026)
Symmetry Breaking in Neural Network Optimization: Insights from Input Dimension Expansion
by: Zhang, Jun-Jie, et al.
Published: (2024)
by: Zhang, Jun-Jie, et al.
Published: (2024)
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism
by: Wang, Zhiwei, et al.
Published: (2024)
by: Wang, Zhiwei, et al.
Published: (2024)
QCPINN: Quantum-Classical Physics-Informed Neural Networks for Solving PDEs
by: Farea, Afrah, et al.
Published: (2025)
by: Farea, Afrah, et al.
Published: (2025)
Adaptive Preconditioners Trigger Loss Spikes in Adam
by: Bai, Zhiwei, et al.
Published: (2025)
by: Bai, Zhiwei, et al.
Published: (2025)
Solving Partial Differential Equations in Different Domains by Operator Learning method Based on Boundary Integral Equations
by: Meng, Bin, et al.
Published: (2024)
by: Meng, Bin, et al.
Published: (2024)
Anchor function: a type of benchmark functions for studying language models
by: Zhang, Zhongwang, et al.
Published: (2024)
by: Zhang, Zhongwang, et al.
Published: (2024)
General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
by: Wang, Sen, et al.
Published: (2024)
by: Wang, Sen, et al.
Published: (2024)
Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
by: Geng, Ru, et al.
Published: (2026)
by: Geng, Ru, et al.
Published: (2026)
Loss-Complexity Landscape and Model Structure Functions
by: Kolpakov, Alexander
Published: (2025)
by: Kolpakov, Alexander
Published: (2025)
Walsh-Hadamard Neural Operators for Solving PDEs with Discontinuous Coefficients
by: Cavallazzi, Giorgio M., et al.
Published: (2025)
by: Cavallazzi, Giorgio M., et al.
Published: (2025)
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
by: Propp, Adrienne M., et al.
Published: (2025)
by: Propp, Adrienne M., et al.
Published: (2025)
Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation
by: Marciano, Antonino, et al.
Published: (2022)
by: Marciano, Antonino, et al.
Published: (2022)
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
by: Bora, Aniruddha, et al.
Published: (2025)
by: Bora, Aniruddha, et al.
Published: (2025)
Accelerated Airfoil Design Using Neural Network Approaches
by: Patel, Anantram, et al.
Published: (2025)
by: Patel, Anantram, et al.
Published: (2025)
Solution of the Probabilistic Lambert Problem: Connections with Optimal Mass Transport, Schrödinger Bridge and Reaction-Diffusion PDEs
by: Teter, Alexis M. H., et al.
Published: (2024)
by: Teter, Alexis M. H., et al.
Published: (2024)
Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
by: Propp, Adrienne M., et al.
Published: (2024)
by: Propp, Adrienne M., et al.
Published: (2024)
A Unified Theory of Quantum Neural Network Loss Landscapes
by: Anschuetz, Eric R.
Published: (2024)
by: Anschuetz, Eric R.
Published: (2024)
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
by: Menon, Sidharth S., et al.
Published: (2026)
by: Menon, Sidharth S., et al.
Published: (2026)
Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation law
by: Zhang, Jingdong, et al.
Published: (2024)
by: Zhang, Jingdong, et al.
Published: (2024)
Symbolic identification of tensor equations in multidimensional physical fields
by: Chen, Tianyi, et al.
Published: (2025)
by: Chen, Tianyi, et al.
Published: (2025)
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
by: Mirzabeigi, Elmira, et al.
Published: (2025)
by: Mirzabeigi, Elmira, et al.
Published: (2025)
PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structure
by: Wu, Junyi, et al.
Published: (2025)
by: Wu, Junyi, et al.
Published: (2025)
Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs
by: Škardová, Kateřina, et al.
Published: (2024)
by: Škardová, Kateřina, et al.
Published: (2024)
Reinforcement Learning for Jump-Diffusions, with Financial Applications
by: Gao, Xuefeng, et al.
Published: (2024)
by: Gao, Xuefeng, et al.
Published: (2024)
Similar Items
-
Loss Spike in Training Neural Networks
by: Li, Xiaolong, et al.
Published: (2023) -
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks
by: Bai, Zhiwei, et al.
Published: (2022) -
The Loss Surfaces of Neural Networks with General Activation Functions
by: Baskerville, Nicholas P., et al.
Published: (2020) -
Initialization is Critical to Whether Transformers Fit Composite Functions by Reasoning or Memorizing
by: Zhang, Zhongwang, et al.
Published: (2024) -
Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
by: Yin, Pengsong, et al.
Published: (2024)