4 citations · 4 across the 3 of their papers we have counts for
4 papers
When Rough Data Helps: A Phase Transition in Convergence Rates for Kernel Recovery in Integral Operators
Jihong Wang, Fei Lu, Yue Yu
Learning kernels in operators from data is a fundamental task that arises in nonlocal continuum mechanics, operator learning, and interacting particle systems. A central question i…
Embedded Nonlocal Operator Regression (ENOR): Quantifying model error in learning nonlocal operators
Yiming Fan, Habib Najm, Yue Yu +2
Nonlocal, integral operators have become an efficient surrogate for bottom-up homogenization, due to their ability to represent long-range dependence and multiscale effects. Howeve…
Peridynamic Neural Operators: A Data-Driven Nonlocal Constitutive Model for Complex Material Responses
Siavash Jafarzadeh, Stewart Silling, Ning Liu +2
Neural operators, which can act as implicit solution operators of hidden governing equations, have recently become popular tools for learning the responses of complex real-world ph…
Harnessing the Power of Neural Operators with Automatically Encoded Conservation Laws
Ning Liu, Yiming Fan, Xianyi Zeng +3
Neural operators (NOs) have emerged as effective tools for modeling complex physical systems in scientific machine learning. In NOs, a central characteristic is to learn the govern…