Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
Fuente:
arXiv
Guardado en:
| Autores principales: | Singh, Ashutosh, Borsoi, Ricardo Augusto, Erdogmus, Deniz, Imbiriba, Tales |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
por: Singh, Ashutosh, et al.
Publicado: (2024)
por: Singh, Ashutosh, et al.
Publicado: (2024)
Recursive Deep Inverse Reinforcement Learning
por: Ghanem, Paul, et al.
Publicado: (2025)
por: Ghanem, Paul, et al.
Publicado: (2025)
Learning Physics Informed Neural ODEs With Partial Measurements
por: Ghanem, Paul, et al.
Publicado: (2024)
por: Ghanem, Paul, et al.
Publicado: (2024)
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
por: Wang, Hong, et al.
Publicado: (2024)
por: Wang, Hong, et al.
Publicado: (2024)
CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
por: Hou, Xianglong, et al.
Publicado: (2025)
por: Hou, Xianglong, et al.
Publicado: (2025)
Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
por: Berner, Julius, et al.
Publicado: (2025)
por: Berner, Julius, et al.
Publicado: (2025)
Neural Operators with Localized Integral and Differential Kernels
por: Liu-Schiaffini, Miguel, et al.
Publicado: (2024)
por: Liu-Schiaffini, Miguel, et al.
Publicado: (2024)
CATO: Charted Attention for Neural PDE Operators
por: Cheng, Chun-Wun, et al.
Publicado: (2026)
por: Cheng, Chun-Wun, et al.
Publicado: (2026)
Data-Efficient Neural Operator Training via Physics-Based Active Learning
por: Polanska, Alicja, et al.
Publicado: (2026)
por: Polanska, Alicja, et al.
Publicado: (2026)
Autoregression-Free Neural Operators for Time-Dependent PDEs
por: Zhang, Jiaquan, et al.
Publicado: (2026)
por: Zhang, Jiaquan, et al.
Publicado: (2026)
Monte Carlo-Type Neural Operator for Differential Equations
por: Choutri, Salah Eddine, et al.
Publicado: (2025)
por: Choutri, Salah Eddine, et al.
Publicado: (2025)
A Mathematical Guide to Operator Learning
por: Boullé, Nicolas, et al.
Publicado: (2023)
por: Boullé, Nicolas, et al.
Publicado: (2023)
Projection Methods for Operator Learning and Universal Approximation
por: Zappala, Emanuele
Publicado: (2024)
por: Zappala, Emanuele
Publicado: (2024)
Beyond Loss Guidance: Using PDE Residuals as Spectral Attention in Diffusion Neural Operators
por: Sawhney, Medha, et al.
Publicado: (2025)
por: Sawhney, Medha, et al.
Publicado: (2025)
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)
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
por: Zhang, Ce, et al.
Publicado: (2023)
por: Zhang, Ce, et al.
Publicado: (2023)
Better Neural PDE Solvers Through Data-Free Mesh Movers
por: Hu, Peiyan, et al.
Publicado: (2023)
por: Hu, Peiyan, et al.
Publicado: (2023)
Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study
por: Patel, Janak M., et al.
Publicado: (2025)
por: Patel, Janak M., et al.
Publicado: (2025)
Neural Operators as Efficient Function Interpolators
por: Niarchos, Vasilis, et al.
Publicado: (2026)
por: Niarchos, Vasilis, et al.
Publicado: (2026)
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
por: Son, Hwijae
Publicado: (2025)
por: Son, Hwijae
Publicado: (2025)
Operator learning without the adjoint
por: Boullé, Nicolas, et al.
Publicado: (2024)
por: Boullé, Nicolas, et al.
Publicado: (2024)
Learning from Integral Losses in Physics Informed Neural Networks
por: Saleh, Ehsan, et al.
Publicado: (2023)
por: Saleh, Ehsan, et al.
Publicado: (2023)
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
por: Mustajab, Abdul Hannan, et al.
Publicado: (2024)
por: Mustajab, Abdul Hannan, et al.
Publicado: (2024)
STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem
por: Wang, Hong, et al.
Publicado: (2025)
por: Wang, Hong, et al.
Publicado: (2025)
DeepContour: A Hybrid Deep Learning Framework for Accelerating Generalized Eigenvalue Problem Solving via Efficient Contour Design
por: Chen, Yeqiu, et al.
Publicado: (2025)
por: Chen, Yeqiu, et al.
Publicado: (2025)
PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws
por: Yang, Liu, et al.
Publicado: (2024)
por: Yang, Liu, et al.
Publicado: (2024)
Data-Driven Prediction of Dynamic Interactions Between Robot Appendage and Granular Material
por: Wang, Guanjin, et al.
Publicado: (2025)
por: Wang, Guanjin, et al.
Publicado: (2025)
Quasi-Random Physics-informed Neural Networks
por: Yu, Tianchi, et al.
Publicado: (2025)
por: Yu, Tianchi, et al.
Publicado: (2025)
Quantum Neural Network Restatement of Markov Jump Process
por: Zarezadeh, Z., et al.
Publicado: (2025)
por: Zarezadeh, Z., et al.
Publicado: (2025)
A Dimensionality Reduction Approach for Convolutional Neural Networks
por: Meneghetti, Laura, et al.
Publicado: (2021)
por: Meneghetti, Laura, et al.
Publicado: (2021)
Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
por: Zhou, Hang, et al.
Publicado: (2024)
por: Zhou, Hang, et al.
Publicado: (2024)
Solving PDEs With Deep Neural Nets under General Boundary Conditions
por: Zhang, Chenggong
Publicado: (2025)
por: Zhang, Chenggong
Publicado: (2025)
Graph-Instructed Neural Networks for parametric problems with varying boundary conditions
por: Della Santa, Francesco, et al.
Publicado: (2026)
por: Della Santa, Francesco, et al.
Publicado: (2026)
Recursive Flow Matching
por: Huang, Jiahe, et al.
Publicado: (2026)
por: Huang, Jiahe, et al.
Publicado: (2026)
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
por: Gao, Yihang, et al.
Publicado: (2024)
por: Gao, Yihang, et al.
Publicado: (2024)
Moving Sampling Physics-informed Neural Networks induced by Moving Mesh PDE
por: Yang, Yu, et al.
Publicado: (2023)
por: Yang, Yu, et al.
Publicado: (2023)
Quasi-Framelets: Robust Graph Neural Networks via Adaptive Framelet Convolution
por: Yang, Mengxi, et al.
Publicado: (2022)
por: Yang, Mengxi, et al.
Publicado: (2022)
Stability and Discretization Error of State Space Model Neural Operators
por: Bendahi, Abderrahim, et al.
Publicado: (2026)
por: Bendahi, Abderrahim, et al.
Publicado: (2026)
Graph Neural Networks for Emulation of Finite-Element Ice Dynamics in Greenland and Antarctic Ice Sheets
por: Koo, Younghyun, et al.
Publicado: (2024)
por: Koo, Younghyun, et al.
Publicado: (2024)
Advancing the Understanding of Fixed Point Iterations in Deep Neural Networks: A Detailed Analytical Study
por: Ke, Yekun, et al.
Publicado: (2024)
por: Ke, Yekun, et al.
Publicado: (2024)
Ejemplares similares
-
KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
por: Singh, Ashutosh, et al.
Publicado: (2024) -
Recursive Deep Inverse Reinforcement Learning
por: Ghanem, Paul, et al.
Publicado: (2025) -
Learning Physics Informed Neural ODEs With Partial Measurements
por: Ghanem, Paul, et al.
Publicado: (2024) -
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling
por: Wang, Hong, et al.
Publicado: (2024) -
CFO: Learning Continuous-Time PDE Dynamics via Flow-Matched Neural Operators
por: Hou, Xianglong, et al.
Publicado: (2025)