Physics-informed Discretization-independent Deep Compositional Operator Network
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhong, Weiheng, Meidani, Hadi |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Physics-Informed Geometry-Aware Neural Operator
por: Zhong, Weiheng, et al.
Publicado: (2024)
por: Zhong, Weiheng, et al.
Publicado: (2024)
Deep NURBS -- Admissible Physics-informed Neural Networks
por: Saidaoui, Hamed, et al.
Publicado: (2022)
por: Saidaoui, Hamed, et al.
Publicado: (2022)
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
por: Wei, Zhi-Feng, et al.
Publicado: (2025)
por: Wei, Zhi-Feng, et al.
Publicado: (2025)
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
por: Wang, Sifan, et al.
Publicado: (2024)
por: Wang, Sifan, et al.
Publicado: (2024)
Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks
por: Liu, Chaoyu, et al.
Publicado: (2023)
por: Liu, Chaoyu, et al.
Publicado: (2023)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
por: Gonon, Lukas, et al.
Publicado: (2024)
por: Gonon, Lukas, et al.
Publicado: (2024)
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
por: Moya, Christian, et al.
Publicado: (2024)
por: Moya, Christian, et al.
Publicado: (2024)
Controlling Statistical, Discretization, and Truncation Errors in Learning Fourier Linear Operators
por: Subedi, Unique, et al.
Publicado: (2024)
por: Subedi, Unique, et al.
Publicado: (2024)
ZNO: Stable Rational Neural Operators in the Z-Domain for Discrete-Time Dynamics
por: Zhu, Xianli, et al.
Publicado: (2026)
por: Zhu, Xianli, et al.
Publicado: (2026)
Size Lowerbounds for Deep Operator Networks
por: Mukherjee, Anirbit, et al.
Publicado: (2023)
por: Mukherjee, Anirbit, et al.
Publicado: (2023)
ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
por: Zheng, Haolan, et al.
Publicado: (2025)
por: Zheng, Haolan, et al.
Publicado: (2025)
Learning Mesh-Free Discrete Differential Operators with Self-Supervised Graph Neural Networks
por: Starepravo, Lucas Gerken, et al.
Publicado: (2026)
por: Starepravo, Lucas Gerken, et al.
Publicado: (2026)
Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Spectral Audit of In-Context Operator Networks
por: Gao, Zhiwei, et al.
Publicado: (2026)
por: Gao, Zhiwei, et al.
Publicado: (2026)
A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
por: Chen, Zhenglong, et al.
Publicado: (2025)
por: Chen, Zhenglong, et al.
Publicado: (2025)
Towards Size-Independent Generalization Bounds for Deep Operator Nets
por: Gopalani, Pulkit, et al.
Publicado: (2022)
por: Gopalani, Pulkit, et al.
Publicado: (2022)
A Discrete Perspective Towards the Construction of Sparse Probabilistic Boolean Networks
por: Fok, Christopher H., et al.
Publicado: (2024)
por: Fok, Christopher H., et al.
Publicado: (2024)
Quasi-Random Physics-informed Neural Networks
por: Yu, Tianchi, et al.
Publicado: (2025)
por: Yu, Tianchi, et al.
Publicado: (2025)
Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
por: Katende, Ronald
Publicado: (2025)
por: Katende, Ronald
Publicado: (2025)
Discrete Solution Operator Learning for Geometry-Dependent PDEs
por: Bai, Jinshuai, et al.
Publicado: (2026)
por: Bai, Jinshuai, et al.
Publicado: (2026)
fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
por: Lu, Binghang, et al.
Publicado: (2026)
por: Lu, Binghang, et al.
Publicado: (2026)
A Deep Learning Framework for Multi-Operator Learning: Architectures and Approximation Theory
por: Weihs, Adrien, et al.
Publicado: (2025)
por: Weihs, Adrien, et al.
Publicado: (2025)
Parameterized Physics-informed Neural Networks for Parameterized PDEs
por: Cho, Woojin, et al.
Publicado: (2024)
por: Cho, Woojin, et al.
Publicado: (2024)
Multi-scale DeepOnet (Mscale-DeepOnet) for Mitigating Spectral Bias in Learning High Frequency Operators of Oscillatory Functions
por: Wang, Bo, et al.
Publicado: (2025)
por: Wang, Bo, et al.
Publicado: (2025)
High-fidelity Multiphysics Modelling for Rapid Predictions Using Physics-informed Parallel Neural Operator
por: Yuan, Biao, et al.
Publicado: (2025)
por: Yuan, Biao, et al.
Publicado: (2025)
Neural Conjugate Flows: Physics-informed architectures with flow structure
por: Bizzi, Arthur, et al.
Publicado: (2024)
por: Bizzi, Arthur, et al.
Publicado: (2024)
Physics-informed reduced order model with conditional neural fields
por: Kim, Minji, et al.
Publicado: (2024)
por: Kim, Minji, et al.
Publicado: (2024)
Physics-informed neural networks for operator equations with stochastic data
por: Escapil-Inchauspé, Paul, et al.
Publicado: (2022)
por: Escapil-Inchauspé, Paul, et al.
Publicado: (2022)
ELM-DeepONets: Backpropagation-Free Training of Deep Operator Networks via Extreme Learning Machines
por: Son, Hwijae
Publicado: (2025)
por: Son, Hwijae
Publicado: (2025)
Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
por: Zeudong, Etienne, et al.
Publicado: (2025)
por: Zeudong, Etienne, et al.
Publicado: (2025)
DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
por: Jiang, Zhaoxi, et al.
Publicado: (2025)
por: Jiang, Zhaoxi, et al.
Publicado: (2025)
Operator SVD with Neural Networks via Nested Low-Rank Approximation
por: Ryu, J. Jon, et al.
Publicado: (2024)
por: Ryu, J. Jon, et al.
Publicado: (2024)
Reduced-Basis Deep Operator Learning for Parametric PDEs with Independently Varying Boundary and Source Data
por: Wang, Yueqi, et al.
Publicado: (2025)
por: Wang, Yueqi, et al.
Publicado: (2025)
Physics-informed sensor coverage through structure preserving machine learning
por: Shaffer, Benjamin David, et al.
Publicado: (2025)
por: Shaffer, Benjamin David, et al.
Publicado: (2025)
Derivative-enhanced Deep Operator Network
por: Qiu, Yuan, et al.
Publicado: (2024)
por: Qiu, Yuan, et al.
Publicado: (2024)
DeepRitzSplit Neural Operator for Phase-Field Models via Energy Splitting
por: Huang, Chih-Kang, et al.
Publicado: (2026)
por: Huang, Chih-Kang, et al.
Publicado: (2026)
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
por: Williams, Emily, et al.
Publicado: (2024)
por: Williams, Emily, et al.
Publicado: (2024)
Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
por: Mahowald, Jamie, et al.
Publicado: (2026)
por: Mahowald, Jamie, et al.
Publicado: (2026)
Approximation with SiLU Networks: Constant Depth and Exponential Rates for Basic Operations
por: Ayena, Koffi O.
Publicado: (2025)
por: Ayena, Koffi O.
Publicado: (2025)
Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
por: Ma, Qinglong, et al.
Publicado: (2024)
por: Ma, Qinglong, et al.
Publicado: (2024)
Ejemplares similares
-
Physics-Informed Geometry-Aware Neural Operator
por: Zhong, Weiheng, et al.
Publicado: (2024) -
Deep NURBS -- Admissible Physics-informed Neural Networks
por: Saidaoui, Hamed, et al.
Publicado: (2022) -
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
por: Wei, Zhi-Feng, et al.
Publicado: (2025) -
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks
por: Wang, Sifan, et al.
Publicado: (2024) -
Inverse Evolution Layers: Physics-informed Regularizers for Deep Neural Networks
por: Liu, Chaoyu, et al.
Publicado: (2023)