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Conformalized-deeponet: A distribution-free framework for uncertainty quantification in deep operator networks
C Moya, A Mollaali, Z Zhang, L Lu, G Lin - Physica D: Nonlinear …, 2025 - Elsevier
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification
(UQ) framework, to obtain prediction intervals with coverage guarantees for Deep Operator …
(UQ) framework, to obtain prediction intervals with coverage guarantees for Deep Operator …
Using uncertainty quantification to characterize and improve out-of-domain learning for PDEs
Uncertainty quantification for DeepONets with ensemble Kalman inversion
A Pensoneault, X Zhu - Journal of computational physics, 2025 - Elsevier
In recent years, operator learning, particularly the DeepONet, has received much attention
for efficiently learning complex map**s between input and output functions across diverse …
for efficiently learning complex map**s between input and output functions across diverse …
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems
SK Boya, D Subramani - arxiv preprint arxiv:2412.09009, 2024 - arxiv.org
Initial boundary value problems arise commonly in applications with engineering and
natural systems governed by nonlinear partial differential equations (PDEs). Operator …
natural systems governed by nonlinear partial differential equations (PDEs). Operator …
[HTML][HTML] Exploring the Trade-Off in the Variational Information Bottleneck for Regression with a Single Training Run
An information bottleneck (IB) enables the acquisition of useful representations from data by
retaining necessary information while reducing unnecessary information. In its objective …
retaining necessary information while reducing unnecessary information. In its objective …
Orthogonal greedy algorithm for linear operator learning with shallow neural network
Y Lin, J Jia, YJ Lee, R Zhang - arxiv preprint arxiv:2501.02791, 2025 - arxiv.org
Greedy algorithms, particularly the orthogonal greedy algorithm (OGA), have proven
effective in training shallow neural networks for fitting functions and solving partial …
effective in training shallow neural networks for fitting functions and solving partial …
A survey on machine learning approaches for uncertainty quantification of engineering systems
Uncertainty quantification (UQ) is essential for understanding and mitigating the impact of
pervasive uncertainties in engineering systems, playing a crucial role in modern …
pervasive uncertainties in engineering systems, playing a crucial role in modern …