175 citations · 317 across the 46 of their papers we have counts for
4 papers · 1 filter
D2NO: Efficient Handling of Heterogeneous Input Function Spaces with Distributed Deep Neural Operators
Zecheng Zhang, Christian Moya, Lu Lu +2
Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. Ho…
PROSE: Predicting Operators and Symbolic Expressions using Multimodal Transformers
Yuxuan Liu, Zecheng Zhang, Hayden Schaeffer
Approximating nonlinear differential equations using a neural network provides a robust and efficient tool for various scientific computing tasks, including real-time predictions,…
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…