2 citations · 3 across the 5 of their papers we have counts for
5 papers
MODNO: Multi Operator Learning With Distributed Neural Operators
Zecheng Zhang
The study of operator learning involves the utilization of neural networks to approximate operators. Traditionally, the focus has been on single-operator learning (SOL). However, r…
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
Christian Moya, Amirhossein Mollaali, Zecheng Zhang +2
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for…
Restoring the Discontinuous Heat Equation Source Using Sparse Boundary Data and Dynamic Sensors
Guang Lin, Na Ou, Zecheng Zhang +1
This study focuses on addressing the inverse source problem associated with the parabolic equation. We rely on sparse boundary flux data as our measurements, which are acquired fro…
Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE
Zecheng Zhang, Christian Moya, Wing Tat Leung +2
We present a new framework for computing fine-scale solutions of multiscale Partial Differential Equations (PDEs) using operator learning tools. Obtaining fine-scale solutions of m…
A discretization-invariant extension and analysis of some deep operator networks
Zecheng Zhang, Wing Tat Leung, Hayden Schaeffer
We present a generalized version of the discretization-invariant neural operator and prove that the network is a universal approximation in the operator sense. Moreover, by incorpo…