40 citations · 61 across the 12 of their papers we have counts for
5 papers · 1 filter
Learning the detector in optical tomography
Zijian Wang, Andreas Hauptmann, Lu Lu +1
We propose a method to reconstruct the optical absorption of a highly-scattering medium probed by diffuse light. The method consists of learning the optical detection system and th…
RAMS: Residual-based adversarial-gradient moving sample method for scientific machine learning in solving partial differential equations
Weihang Ouyang, Min Zhu, Wei Xiong +2
Physics-informed neural networks (PINNs) and neural operators, two leading scientific machine learning (SciML) paradigms, have emerged as powerful tools for solving partial differe…
Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
Ryan Bausback, Jingqiao Tang, Lu Lu +2
We develop a novel framework for uncertainty quantification in operator learning, the Stochastic Operator Network (SON). SON combines the stochastic optimal control concepts of the…
Neural-operator element method: Efficient and scalable finite element method enabled by reusable neural operators
Weihang Ouyang, Yeonjong Shin, Si-Wei Liu +1
The finite element method (FEM) is a well-established numerical method for solving partial differential equations (PDEs). However, its mesh-based nature gives rise to substantial c…
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Xiaobo Xia, Xiaofeng Liu, Jiale Liu +5
Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality…