A novel data generation scheme for surrogate modelling with deep operator networks
Fuente:
arXiv
Guardado en:
| Autores principales: | Choubey, Shivam, Pal, Birupaksha, Agrawal, Manish |
|---|---|
| Formato: | Preprint |
| Publicado: |
2024
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A novel framework for generalization of deep hidden physics models
por: Kag, Vijay, et al.
Publicado: (2024)
por: Kag, Vijay, et al.
Publicado: (2024)
Deep adaptive sampling for surrogate modeling without labeled data
por: Wang, Xili, et al.
Publicado: (2024)
por: Wang, Xili, et al.
Publicado: (2024)
Resolution invariant deep operator network for PDEs with complex geometries
por: Huang, Jianguo, et al.
Publicado: (2024)
por: Huang, Jianguo, et al.
Publicado: (2024)
Mesh motion in fluid-structure interaction with deep operator networks
por: Hellan, Ottar
Publicado: (2024)
por: Hellan, Ottar
Publicado: (2024)
Neural and spectral operator surrogates: unified construction and expression rate bounds
por: Herrmann, Lukas, et al.
Publicado: (2022)
por: Herrmann, Lukas, et al.
Publicado: (2022)
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
por: Chen, Wei, et al.
Publicado: (2025)
por: Chen, Wei, et al.
Publicado: (2025)
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
por: Zhang, Benjamin J., et al.
Publicado: (2025)
por: Zhang, Benjamin J., et al.
Publicado: (2025)
Shape-informed surrogate models based on signed distance function domain encoding
por: Zhang, Linying, et al.
Publicado: (2024)
por: Zhang, Linying, 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)
On the expressivity of deep Heaviside networks
por: Kong, Insung, et al.
Publicado: (2025)
por: Kong, Insung, et al.
Publicado: (2025)
Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression
por: Kapoor, Sarthak, et al.
Publicado: (2022)
por: Kapoor, Sarthak, et al.
Publicado: (2022)
Shape-informed cardiac mechanics surrogates in data-scarce regimes via geometric encoding and generative augmentation
por: Carrara, Davide, et al.
Publicado: (2026)
por: Carrara, Davide, et al.
Publicado: (2026)
Variational Bayesian surrogate modelling with application to robust design optimisation
por: Archbold, Thomas A., et al.
Publicado: (2024)
por: Archbold, Thomas A., et al.
Publicado: (2024)
Data-Augmented Predictive Deep Neural Network: Enhancing the extrapolation capabilities of non-intrusive surrogate models
por: Sun, Shuwen, et al.
Publicado: (2024)
por: Sun, Shuwen, et al.
Publicado: (2024)
Multi-fidelity surrogate with heterogeneous input spaces for modeling melt pools in laser-directed energy deposition
por: Menon, Nandana, et al.
Publicado: (2024)
por: Menon, Nandana, et al.
Publicado: (2024)
Optimal deep learning of holomorphic operators between Banach spaces
por: Adcock, Ben, et al.
Publicado: (2024)
por: Adcock, Ben, et al.
Publicado: (2024)
A data augmentation strategy for deep neural networks with application to epidemic modelling
por: Awais, Muhammad, et al.
Publicado: (2025)
por: Awais, Muhammad, et al.
Publicado: (2025)
Memorization capacity of deep ReLU neural networks characterized by width and depth
por: Yang, Xin, et al.
Publicado: (2026)
por: Yang, Xin, et al.
Publicado: (2026)
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
por: Yang, Yunfei
Publicado: (2024)
por: Yang, Yunfei
Publicado: (2024)
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
por: Adcock, Ben, et al.
Publicado: (2024)
por: Adcock, Ben, et al.
Publicado: (2024)
Approximation and learning of anisotropic and mixed smooth functions by deep ReLU neural networks
por: Yang, Yunfei, et al.
Publicado: (2026)
por: Yang, Yunfei, et al.
Publicado: (2026)
Orthogonal greedy algorithm for linear operator learning with shallow neural network
por: Lin, Ye, et al.
Publicado: (2025)
por: Lin, Ye, et al.
Publicado: (2025)
Nonlinear model reduction for operator learning
por: Eivazi, Hamidreza, et al.
Publicado: (2024)
por: Eivazi, Hamidreza, et al.
Publicado: (2024)
Generating synthetic data for neural operators
por: Hasani, Erisa, et al.
Publicado: (2024)
por: Hasani, Erisa, et al.
Publicado: (2024)
GFN: A graph feedforward network for resolution-invariant reduced operator learning in multifidelity applications
por: Morrison, Oisín M., et al.
Publicado: (2024)
por: Morrison, Oisín M., et al.
Publicado: (2024)
Coupling the reduced-order model and the generative model for an importance sampling estimator
por: Wan, Xiaoliang, et al.
Publicado: (2019)
por: Wan, Xiaoliang, et al.
Publicado: (2019)
Flexible SE(2) graph neural networks with applications to PDE surrogates
por: Bånkestad, Maria, et al.
Publicado: (2024)
por: Bånkestad, Maria, et al.
Publicado: (2024)
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
por: Jentzen, Arnulf, et al.
Publicado: (2023)
por: Jentzen, Arnulf, et al.
Publicado: (2023)
A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
por: Li, Hongyan, et al.
Publicado: (2022)
por: Li, Hongyan, et al.
Publicado: (2022)
Filtered Neural Galerkin model reduction schemes for efficient propagation of initial condition uncertainties in digital twins
por: Ning, Zhiyang, et al.
Publicado: (2025)
por: Ning, Zhiyang, et al.
Publicado: (2025)
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
por: Wang, Yizheng, et al.
Publicado: (2024)
por: Wang, Yizheng, et al.
Publicado: (2024)
Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
por: Zacchei, Filippo, et al.
Publicado: (2025)
por: Zacchei, Filippo, et al.
Publicado: (2025)
Wasserstein Bounds for generative diffusion models with Gaussian tail targets
por: Wang, Xixian, et al.
Publicado: (2024)
por: Wang, Xixian, et al.
Publicado: (2024)
Wasserstein approximation schemes based on Voronoi partitions
por: Hamm, Keaton, et al.
Publicado: (2023)
por: Hamm, Keaton, et al.
Publicado: (2023)
Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems
por: Williams, Emily, et al.
Publicado: (2025)
por: Williams, Emily, et al.
Publicado: (2025)
Space-time deep neural network approximations for high-dimensional partial differential equations
por: Hornung, Fabian, et al.
Publicado: (2020)
por: Hornung, Fabian, et al.
Publicado: (2020)
A deep learning-based surrogate model for seismic data assimilation in fault activation modeling
por: Millevoi, Caterina, et al.
Publicado: (2024)
por: Millevoi, Caterina, et al.
Publicado: (2024)
When big data actually are low-rank, or entrywise approximation of certain function-generated matrices
por: Budzinskiy, Stanislav
Publicado: (2024)
por: Budzinskiy, Stanislav
Publicado: (2024)
LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport
por: Cao, Lianghao, et al.
Publicado: (2024)
por: Cao, Lianghao, et al.
Publicado: (2024)
PTPI-DL-ROMs: pre-trained physics-informed deep learning-based reduced order models for nonlinear parametrized PDEs
por: Brivio, Simone, et al.
Publicado: (2024)
por: Brivio, Simone, et al.
Publicado: (2024)
Ejemplares similares
-
A novel framework for generalization of deep hidden physics models
por: Kag, Vijay, et al.
Publicado: (2024) -
Deep adaptive sampling for surrogate modeling without labeled data
por: Wang, Xili, et al.
Publicado: (2024) -
Resolution invariant deep operator network for PDEs with complex geometries
por: Huang, Jianguo, et al.
Publicado: (2024) -
Mesh motion in fluid-structure interaction with deep operator networks
por: Hellan, Ottar
Publicado: (2024) -
Neural and spectral operator surrogates: unified construction and expression rate bounds
por: Herrmann, Lukas, et al.
Publicado: (2022)