14 citations · 42 across the 25 of their papers we have counts for
5 papers · 1 filter
fPINN-DeepONet: A Physics-Informed Operator Learning Framework for Multi-term Time-fractional Mixed Diffusion-wave Equations
Binghang Lu, Zhaopeng Hao, Christian Moya +1
In this paper, we develop a physics-informed deep operator learning framework for solving multi-term time-fractional mixed diffusion-wave equations (TFMDWEs). We begin by deriving…
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…
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…
DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories
Christian Moya, Shiqi Zhang, Meng Yue +1
This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories. The proposed method is based on the fundamentally new concept of D…
Accelerated replica exchange stochastic gradient Langevin diffusion enhanced Bayesian DeepONet for solving noisy parametric PDEs
Guang Lin, Christian Moya, Zecheng Zhang
The Deep Operator Networks~(DeepONet) is a fundamentally different class of neural networks that we train to approximate nonlinear operators, including the solution operator of par…