Saved in:
| Main Authors: | Ng, Jakin, Wang, Yongji, Lai, Ching-Yao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.17213 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Resolving Sharp Gradients of Unstable Singularities to Machine Precision via Neural Networks
by: Wang, Yongji, et al.
Published: (2025)
by: Wang, Yongji, et al.
Published: (2025)
Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025)
by: Zhou, Chenhong, et al.
Published: (2025)
Approximation of RKHS Functionals by Neural Networks
by: Zhou, Tian-Yi, et al.
Published: (2024)
by: Zhou, Tian-Yi, et al.
Published: (2024)
Multiscale Training of Convolutional Neural Networks
by: Ahamed, Shadab, et al.
Published: (2025)
by: Ahamed, Shadab, et al.
Published: (2025)
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
by: Dangel, Felix, et al.
Published: (2024)
by: Dangel, Felix, et al.
Published: (2024)
Robust Parameter and State Estimation in Multiscale Neuronal Systems Using Physics-Informed Neural Networks
by: Wei, Changliang, et al.
Published: (2026)
by: Wei, Changliang, et al.
Published: (2026)
EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
by: Eivazi, Hamidreza, et al.
Published: (2025)
by: Eivazi, Hamidreza, et al.
Published: (2025)
Functional Tensor Decompositions for Physics-Informed Neural Networks
by: Vemuri, Sai Karthikeya, et al.
Published: (2024)
by: Vemuri, Sai Karthikeya, et al.
Published: (2024)
Approximating Families of Sharp Solutions to Fisher's Equation with Physics-Informed Neural Networks
by: Rohrhofer, Franz M., et al.
Published: (2024)
by: Rohrhofer, Franz M., et al.
Published: (2024)
Multiscale Physics-Informed Neural Network for Complex Fluid Flows with Long-Range Dependencies
by: Kumar, Prashant, et al.
Published: (2026)
by: Kumar, Prashant, et al.
Published: (2026)
Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
by: Gupta, Deepak, et al.
Published: (2025)
by: Gupta, Deepak, et al.
Published: (2025)
Optimal Neural Network Approximation for High-Dimensional Continuous Functions
by: Maiti, Ayan, et al.
Published: (2024)
by: Maiti, Ayan, et al.
Published: (2024)
Improving Set Function Approximation with Quasi-Arithmetic Neural Networks
by: Tokar, Tomas, et al.
Published: (2026)
by: Tokar, Tomas, et al.
Published: (2026)
On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
by: Rosenzweig, Benjamin K., et al.
Published: (2025)
by: Rosenzweig, Benjamin K., et al.
Published: (2025)
Full-Spectrum Graph Neural Networks: Expressive and Scalable
by: Wang, Xiaohan, et al.
Published: (2026)
by: Wang, Xiaohan, et al.
Published: (2026)
Neural Networks with (Low-Precision) Polynomial Approximations: New Insights and Techniques for Accuracy Improvement
by: Zhang, Chi, et al.
Published: (2024)
by: Zhang, Chi, et al.
Published: (2024)
Development of Multistage Machine Learning Classifier using Decision Trees and Boosting Algorithms over Darknet Network Traffic
by: Nair, Anjali Sureshkumar, et al.
Published: (2024)
by: Nair, Anjali Sureshkumar, et al.
Published: (2024)
Guaranteed Approximation Bounds for Mixed-Precision Neural Operators
by: Tu, Renbo, et al.
Published: (2023)
by: Tu, Renbo, et al.
Published: (2023)
SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
by: Gao, Yihang, et al.
Published: (2022)
by: Gao, Yihang, et al.
Published: (2022)
Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)
by: Liu, Songming, et al.
Published: (2024)
Approximation Bounds for Transformer Networks with Application to Regression
by: Jiao, Yuling, et al.
Published: (2025)
by: Jiao, Yuling, et al.
Published: (2025)
Neural Precision Polarization: Simplifying Neural Network Inference with Dual-Level Precision
by: Jayasuriya, Dinithi, et al.
Published: (2024)
by: Jayasuriya, Dinithi, et al.
Published: (2024)
Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
by: Liu, Hao, et al.
Published: (2024)
by: Liu, Hao, et al.
Published: (2024)
Multistage Conditional Compositional Optimization
by: Şen, Buse, et al.
Published: (2026)
by: Şen, Buse, et al.
Published: (2026)
Quanvolutional Neural Networks for Spectrum Peak-Finding
by: Bischof, Lukas, et al.
Published: (2025)
by: Bischof, Lukas, et al.
Published: (2025)
Approximating Matrix Functions with Deep Neural Networks and Transformers
by: Padmanabhan, Rahul, et al.
Published: (2026)
by: Padmanabhan, Rahul, et al.
Published: (2026)
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
by: Chen, Zhiyi, et al.
Published: (2023)
by: Chen, Zhiyi, et al.
Published: (2023)
On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions
by: Lu, Yulong, et al.
Published: (2025)
by: Lu, Yulong, et al.
Published: (2025)
Towards Efficient Training of Graph Neural Networks: A Multiscale Approach
by: Gal, Eshed, et al.
Published: (2025)
by: Gal, Eshed, et al.
Published: (2025)
General Loss Functions Lead to (Approximate) Interpolation in High Dimensions
by: Lai, Kuo-Wei, et al.
Published: (2023)
by: Lai, Kuo-Wei, et al.
Published: (2023)
Approximation Bounds for Recurrent Neural Networks with Application to Regression
by: Jiao, Yuling, et al.
Published: (2024)
by: Jiao, Yuling, et al.
Published: (2024)
Precise Bayesian Neural Networks
by: Brito, Carlos Stein
Published: (2025)
by: Brito, Carlos Stein
Published: (2025)
Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction
by: Jiang, Hongjie, et al.
Published: (2026)
by: Jiang, Hongjie, et al.
Published: (2026)
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications
by: Haywood-Alexander, Marcus, et al.
Published: (2023)
by: Haywood-Alexander, Marcus, et al.
Published: (2023)
FC-PINO: High Precision Physics-Informed Neural Operators via Fourier Continuation
by: Ganeshram, Adarsh, et al.
Published: (2022)
by: Ganeshram, Adarsh, et al.
Published: (2022)
Physics-Informed Machine Learning for Transformer Condition Monitoring -- Part II: Physics-Informed Neural Networks and Uncertainty Quantification
by: Aizpurua, Jose I.
Published: (2025)
by: Aizpurua, Jose I.
Published: (2025)
Probabilistic Functional Neural Networks
by: Wang, Haixu, et al.
Published: (2025)
by: Wang, Haixu, et al.
Published: (2025)
Convex Loss Functions for Support Vector Machines (SVMs) and Neural Networks
by: Portera, Filippo
Published: (2026)
by: Portera, Filippo
Published: (2026)
Multigrade Neural Network Approximation
by: Zhang, Shijun, et al.
Published: (2026)
by: Zhang, Shijun, et al.
Published: (2026)
Logic-Guided Multistage Inference for Explainable Multidefendant Judgment Prediction
by: Zhang, Xu, et al.
Published: (2026)
by: Zhang, Xu, et al.
Published: (2026)
Similar Items
-
Resolving Sharp Gradients of Unstable Singularities to Machine Precision via Neural Networks
by: Wang, Yongji, et al.
Published: (2025) -
Dual-Balancing for Physics-Informed Neural Networks
by: Zhou, Chenhong, et al.
Published: (2025) -
Approximation of RKHS Functionals by Neural Networks
by: Zhou, Tian-Yi, et al.
Published: (2024) -
Multiscale Training of Convolutional Neural Networks
by: Ahamed, Shadab, et al.
Published: (2025) -
Kronecker-Factored Approximate Curvature for Physics-Informed Neural Networks
by: Dangel, Felix, et al.
Published: (2024)