Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs
Fuente:
arXiv
Guardado en:
| Autores principales: | Menon, Sidharth S., Jagtap, Ameya D. |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
por: Menon, Sidharth S., et al.
Publicado: (2026)
por: Menon, Sidharth S., et al.
Publicado: (2026)
BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics
por: Zhang, Yunhao, et al.
Publicado: (2025)
por: Zhang, Yunhao, et al.
Publicado: (2025)
Curse of Dimensionality in Neural Network Optimization
por: Na, Sanghoon, et al.
Publicado: (2025)
por: Na, Sanghoon, et al.
Publicado: (2025)
RiemannONets: Interpretable Neural Operators for Riemann Problems
por: Peyvan, Ahmad, et al.
Publicado: (2024)
por: Peyvan, Ahmad, et al.
Publicado: (2024)
Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
por: Lakshman, Vihan, et al.
Publicado: (2025)
por: Lakshman, Vihan, et al.
Publicado: (2025)
Separable DeepONet: Breaking the Curse of Dimensionality in Physics-Informed Machine Learning
por: Mandl, Luis, et al.
Publicado: (2024)
por: Mandl, Luis, et al.
Publicado: (2024)
In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes
por: Mondal, Trishit, et al.
Publicado: (2026)
por: Mondal, Trishit, et al.
Publicado: (2026)
Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks
por: Hu, Zheyuan, et al.
Publicado: (2024)
por: Hu, Zheyuan, et al.
Publicado: (2024)
Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect
por: Peng, Dehua, et al.
Publicado: (2023)
por: Peng, Dehua, et al.
Publicado: (2023)
SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionality
por: Chakar, Joseph
Publicado: (2024)
por: Chakar, Joseph
Publicado: (2024)
Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertainty
por: Mondal, Trishit, et al.
Publicado: (2026)
por: Mondal, Trishit, et al.
Publicado: (2026)
The Blessing and Curse of Dimensionality in Safety Alignment
por: Teo, Rachel S. Y., et al.
Publicado: (2025)
por: Teo, Rachel S. Y., et al.
Publicado: (2025)
Curse of High Dimensionality Issue in Transformer for Long-context Modeling
por: Zhang, Shuhai, et al.
Publicado: (2025)
por: Zhang, Shuhai, et al.
Publicado: (2025)
Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon
por: Liang, Tongtong, et al.
Publicado: (2025)
por: Liang, Tongtong, et al.
Publicado: (2025)
Overcoming the Curse of Dimensionality in Reinforcement Learning Through Approximate Factorization
por: Lu, Chenbei, et al.
Publicado: (2024)
por: Lu, Chenbei, et al.
Publicado: (2024)
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty
por: Shustin, Paz Fink, et al.
Publicado: (2022)
por: Shustin, Paz Fink, et al.
Publicado: (2022)
Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learning
por: Mondal, Trishit, et al.
Publicado: (2025)
por: Mondal, Trishit, et al.
Publicado: (2025)
An Approximation Theory Perspective on Machine Learning
por: Mhaskar, Hrushikesh N., et al.
Publicado: (2025)
por: Mhaskar, Hrushikesh N., et al.
Publicado: (2025)
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery
por: Liu, Ning, et al.
Publicado: (2025)
por: Liu, Ning, et al.
Publicado: (2025)
CNNs Avoid Curse of Dimensionality by Learning on Patches
por: Madala, Vamshi C., et al.
Publicado: (2022)
por: Madala, Vamshi C., et al.
Publicado: (2022)
How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning
por: Jacot, Arthur, et al.
Publicado: (2024)
por: Jacot, Arthur, et al.
Publicado: (2024)
Mixture of Experts Softens the Curse of Dimensionality in Operator Learning
por: Kratsios, Anastasis, et al.
Publicado: (2024)
por: Kratsios, Anastasis, et al.
Publicado: (2024)
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks
por: Hu, Zheyuan, et al.
Publicado: (2023)
por: Hu, Zheyuan, et al.
Publicado: (2023)
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
por: Abbasi, Jassem, et al.
Publicado: (2025)
por: Abbasi, Jassem, et al.
Publicado: (2025)
High-Dimensional Surrogate Modeling for Closed-Loop Learning of Neural-Network-Parameterized Model Predictive Control
por: Hirt, Sebastian, et al.
Publicado: (2025)
por: Hirt, Sebastian, et al.
Publicado: (2025)
Localized PCA-Net Neural Operators for Scalable Solution Reconstruction of Elliptic PDEs
por: Dhingra, Mrigank, et al.
Publicado: (2025)
por: Dhingra, Mrigank, et al.
Publicado: (2025)
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode
por: Nueve, Enrique, et al.
Publicado: (2024)
por: Nueve, Enrique, et al.
Publicado: (2024)
Mitigating the Curse of Dimensionality for Certified Robustness via Dual Randomized Smoothing
por: Xia, Song, et al.
Publicado: (2024)
por: Xia, Song, et al.
Publicado: (2024)
HEATNETs: Explainable Random Feature Neural Networks for High-Dimensional Parabolic PDEs
por: Georgiou, Kyriakos, et al.
Publicado: (2025)
por: Georgiou, Kyriakos, et al.
Publicado: (2025)
Stabilizing Test-Time Adaptation of High-Dimensional Simulation Surrogates via D-Optimal Statistics
por: Zimmel, Anna, et al.
Publicado: (2026)
por: Zimmel, Anna, et al.
Publicado: (2026)
Deep Learning-Accelerated Surrogate Optimization for High-Dimensional Well Control in Stress-Sensitive Reservoirs
por: Valiyev, Mahammad, et al.
Publicado: (2026)
por: Valiyev, Mahammad, et al.
Publicado: (2026)
Adaptive-Distribution Randomized Neural Networks for PDEs: A Low-Dimensional Distribution-Learning Framework
por: Yang, You, et al.
Publicado: (2026)
por: Yang, You, et al.
Publicado: (2026)
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
por: Wen, Shizheng, et al.
Publicado: (2025)
por: Wen, Shizheng, et al.
Publicado: (2025)
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
por: Fang, Yaxin, et al.
Publicado: (2024)
por: Fang, Yaxin, et al.
Publicado: (2024)
Interpreting Black-box Machine Learning Models for High Dimensional Datasets
por: Karim, Md. Rezaul, et al.
Publicado: (2022)
por: Karim, Md. Rezaul, et al.
Publicado: (2022)
Interpretation of High-Dimensional Regression Coefficients by Comparison with Linearized Compressing Features
por: Schaeffer, Joachim, et al.
Publicado: (2024)
por: Schaeffer, Joachim, et al.
Publicado: (2024)
DRIFT-Net: A Spectral--Coupled Neural Operator for PDEs Learning
por: Li, Jiayi, et al.
Publicado: (2025)
por: Li, Jiayi, et al.
Publicado: (2025)
Neural Score Matching for High-Dimensional Causal Inference
por: Clivio, Oscar, et al.
Publicado: (2022)
por: Clivio, Oscar, et al.
Publicado: (2022)
Unbiased and Second-Order-Free Training for High-Dimensional PDEs
por: Seo, Jaemin, et al.
Publicado: (2026)
por: Seo, Jaemin, et al.
Publicado: (2026)
GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data
por: Margeloiu, Andrei, et al.
Publicado: (2022)
por: Margeloiu, Andrei, et al.
Publicado: (2022)
Ejemplares similares
-
FEKAN: Feature-Enriched Kolmogorov-Arnold Networks
por: Menon, Sidharth S., et al.
Publicado: (2026) -
BubbleOKAN: A Physics-Informed Interpretable Neural Operator for High-Frequency Bubble Dynamics
por: Zhang, Yunhao, et al.
Publicado: (2025) -
Curse of Dimensionality in Neural Network Optimization
por: Na, Sanghoon, et al.
Publicado: (2025) -
RiemannONets: Interpretable Neural Operators for Riemann Problems
por: Peyvan, Ahmad, et al.
Publicado: (2024) -
Breaking the Curse of Dimensionality: On the Stability of Modern Vector Retrieval
por: Lakshman, Vihan, et al.
Publicado: (2025)