Buchumschlag

A numerical study into neural network surrogate model performance for uncertainty propagation

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Wade, Noah, Teferra, Kirubel
Format:	Preprint
Veröffentlicht:	2026
Schlagworte:	Machine Learning
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
von: Bieringer, Sebastian, et al.
Veröffentlicht: (2023)

Efficient dataset construction using active learning and uncertainty-aware neural networks for plasma turbulent transport surrogate models
von: Ho, Aaron, et al.
Veröffentlicht: (2025)

Uncertainty propagation in feed-forward neural network models
von: Diamzon, Jeremy, et al.
Veröffentlicht: (2025)

Development of a graph neural network surrogate for travel demand modelling
von: Makarov, Nikita, et al.
Veröffentlicht: (2024)

Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
von: Chen, Wei, et al.
Veröffentlicht: (2025)

Formulations and scalability of neural network surrogates in nonlinear optimization problems
von: Parker, Robert B., et al.
Veröffentlicht: (2024)

Low-rank surrogate modeling and stochastic zero-order optimization for training of neural networks with black-box layers
von: Chertkov, Andrei, et al.
Veröffentlicht: (2025)

Efficient kernel surrogates for neural network-based regression
von: Qadeer, Saad, et al.
Veröffentlicht: (2023)

On the uncertainty principle of neural networks
von: Zhang, Jun-Jie, et al.
Veröffentlicht: (2022)

Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics
von: Kilicsoy, A. O. M., et al.
Veröffentlicht: (2024)

Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates
von: Nartallo-Kaluarachchi, Ramón, et al.
Veröffentlicht: (2025)

Parameter uncertainties for imperfect surrogate models in the low-noise regime
von: Swinburne, Thomas D, et al.
Veröffentlicht: (2024)

On weight and variance uncertainty in neural networks for regression tasks
von: Monemi, Moein, et al.
Veröffentlicht: (2025)

Distill n' Explain: explaining graph neural networks using simple surrogates
von: Pereira, Tamara, et al.
Veröffentlicht: (2023)

Deep encoder-decoder hierarchical convolutional neural networks for conjugate heat transfer surrogate modeling
von: Ebbs-Picken, Takiah, et al.
Veröffentlicht: (2023)

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks
von: Zhang, Zhiyu, et al.
Veröffentlicht: (2024)

Dimensionality reduction can be used as a surrogate model for high-dimensional forward uncertainty quantification
von: Kim, Jungho, et al.
Veröffentlicht: (2024)

Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows
von: Mao, Runze, et al.
Veröffentlicht: (2025)

A prediction rigidity formalism for low-cost uncertainties in trained neural networks
von: Bigi, Filippo, et al.
Veröffentlicht: (2024)

Randomness and signal propagation in physics-informed neural networks (PINNs): A neural PDE perspective
von: Tucny, Jean-Michel, et al.
Veröffentlicht: (2025)

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
von: Zhuang, Bozhou, et al.
Veröffentlicht: (2024)

Acceleration of horizontal numerical advection for atmospheric modeling through surrogate modeling with temporal coarse-graining
von: Park, Manho, et al.
Veröffentlicht: (2026)

Flexible SE(2) graph neural networks with applications to PDE surrogates
von: Bånkestad, Maria, et al.
Veröffentlicht: (2024)

Multi-fidelity surrogates for mechanics of composites: from co-kriging to multi-fidelity neural networks
von: Wen, Haizhou, et al.
Veröffentlicht: (2026)

Operational risk quantification of power grids using graph neural network surrogates of the DC OPF
von: Zhang, Yadong, et al.
Veröffentlicht: (2023)

Analytical results for uncertainty propagation through trained machine learning regression models
von: Thompson, Andrew
Veröffentlicht: (2024)

Deep operator network for surrogate modeling of poroelasticity with random permeability fields
von: Park, Sangjoon, et al.
Veröffentlicht: (2025)

Improved uncertainty quantification for neural networks with Bayesian last layer
von: Fiedler, Felix, et al.
Veröffentlicht: (2023)

Explainability through uncertainty: Trustworthy decision-making with neural networks
von: Thuy, Arthur, et al.
Veröffentlicht: (2024)

Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected
von: Zhang, Yingtao, et al.
Veröffentlicht: (2025)

Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
von: Thuy, Arthur, et al.
Veröffentlicht: (2024)

A novel data generation scheme for surrogate modelling with deep operator networks
von: Choubey, Shivam, et al.
Veröffentlicht: (2024)

Anti-Money Laundering Systems Using Deep Learning
von: Sidiq, Mashkhal Abdalwahid, et al.
Veröffentlicht: (2025)

Bayesian optimized deep ensemble for uncertainty quantification of deep neural networks: a system safety case study on sodium fast reactor thermal stratification modeling
von: Abulawi, Zaid, et al.
Veröffentlicht: (2024)

Forecasting infectious disease prevalence with associated uncertainty using neural networks
von: Morris, Michael
Veröffentlicht: (2024)

Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
von: Zheng, Shuwen, et al.
Veröffentlicht: (2024)

A convolutional autoencoder and neural ODE framework for surrogate modeling of transient counterflow flames
von: Baykan, Mert Yakup, et al.
Veröffentlicht: (2026)

Nonlinear spiked covariance matrices and signal propagation in deep neural networks
von: Wang, Zhichao, et al.
Veröffentlicht: (2024)

Discovering uncertainty: Gaussian constitutive neural networks with correlated weights
von: McCulloch, Jeremy A., et al.
Veröffentlicht: (2025)

Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models
von: Testolin, Alberto
Veröffentlicht: (2023)