:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Gao, Yiheng, Hua, Qin, Chen, Zizhong
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Numerical Analysis
Online Access:	https://arxiv.org/abs/2602.08043
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing
by: Theilman, Bradley H., et al.
Published: (2026)

DeepContour: A Hybrid Deep Learning Framework for Accelerating Generalized Eigenvalue Problem Solving via Efficient Contour Design
by: Chen, Yeqiu, et al.
Published: (2025)

Scaling physics-informed hard constraints with mixture-of-experts
by: Chalapathi, Nithin, et al.
Published: (2024)

Truncated Matrix Completion - An Empirical Study
by: Naik, Rishhabh, et al.
Published: (2025)

Deep Learning-Enhanced Preconditioning for Efficient Conjugate Gradient Solvers in Large-Scale PDE Systems
by: Li, Rui, et al.
Published: (2024)

Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution
by: Yang, Mengxi, et al.
Published: (2022)

PRISM: Distribution-free Adaptive Computation of Matrix Functions for Accelerating Neural Network Training
by: Yang, Shenghao, et al.
Published: (2026)

Unveiling the Power of Multiple Gossip Steps: A Stability-Based Generalization Analysis in Decentralized Training
by: Li, Qinglun, et al.
Published: (2025)

ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
by: Son, Hwijae
Published: (2025)

Accelerating Matrix Diagonalization through Decision Transformers with Epsilon-Greedy Optimization
by: Bhatta, Kshitij, et al.
Published: (2024)

Towards Faster Matrix Diagonalization with Graph Isomorphism Networks and the AlphaZero Framework
by: Zollicoffer, Geigh, et al.
Published: (2024)

BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs
by: Liu, Jerry, et al.
Published: (2025)

Mixed precision accumulation for neural network inference guided by componentwise forward error analysis
by: Arar, El-Mehdi El, et al.
Published: (2025)

SPIKE: Stable Physics-Informed Kernel Evolution Method for Solving Hyperbolic Conservation Laws
by: Su, Hua, et al.
Published: (2025)

On Uniform Weighted Deep Polynomial approximation
by: Yeon, Kingsley, et al.
Published: (2025)

Solving PDEs With Deep Neural Nets under General Boundary Conditions
by: Zhang, Chenggong
Published: (2025)

Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
by: Ke, Yekun, et al.
Published: (2024)

Why Does Stochastic Gradient Descent Slow Down in Low-Precision Training?
by: Yun, Vincent-Daniel
Published: (2025)

ProFlow: Zero-Shot Physics-Consistent Sampling via Proximal Flow Guidance
by: Yu, Zichao, et al.
Published: (2026)

eXmY: A Data Type and Technique for Arbitrary Bit Precision Quantization
by: Agrawal, Aditya, et al.
Published: (2024)

A Mathematical Guide to Operator Learning
by: Boullé, Nicolas, et al.
Published: (2023)

Learning to Discover Iterative Spectral Algorithms
by: Liu, Zihang, et al.
Published: (2026)

Projection Methods for Operator Learning and Universal Approximation
by: Zappala, Emanuele
Published: (2024)

Autoregression-Free Neural Operators for Time-Dependent PDEs
by: Zhang, Jiaquan, et al.
Published: (2026)

Mixture of Experts Softens the Curse of Dimensionality in Operator Learning
by: Kratsios, Anastasis, et al.
Published: (2024)

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
by: Jentzen, Arnulf, et al.
Published: (2023)

Learning from Integral Losses in Physics Informed Neural Networks
by: Saleh, Ehsan, et al.
Published: (2023)

Kinetic-based regularization: Learning spatial derivatives and PDE applications
by: Ganguly, Abhisek, et al.
Published: (2026)

Branching Strategies Based on Subgraph GNNs: A Study on Theoretical Promise versus Practical Reality
by: Zhou, Junru, et al.
Published: (2025)

Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
by: Zhang, Ce, et al.
Published: (2023)

Bayesian Matrix Decomposition and Applications
by: Lu, Jun
Published: (2023)

Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
by: Berman, Jules, et al.
Published: (2026)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
by: Berner, Julius, et al.
Published: (2025)

Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
by: Singh, Ashutosh, et al.
Published: (2024)

CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
by: Hou, Xianglong, et al.
Published: (2025)

Learning to Relax: Setting Solver Parameters Across a Sequence of Linear System Instances
by: Khodak, Mikhail, et al.
Published: (2023)

AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
by: Gao, Yihang, et al.
Published: (2024)

Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
by: Mustajab, Abdul Hannan, et al.
Published: (2024)

Stable Weight Updating: A Key to Reliable PDE Solutions Using Deep Learning
by: Noorizadegan, A., et al.
Published: (2024)

Data-Efficient Neural Operator Training via Physics-Based Active Learning
by: Polanska, Alicja, et al.
Published: (2026)