1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
Data-Driven Model Reduction using WeldNet: Windowed Encoders for Learning Dynamics
Biraj Dahal, Jiahui Cheng, Hao Liu +2
Many problems in science and engineering involve time-dependent, high dimensional datasets arising from complex physical processes, which are costly to simulate. In this work, we p…
IDENT Review: Recent Advances in Identification of Differential Equations from Noisy Data
Roy Y. He, Hao Liu, Wenjing Liao +1
Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the und…
Identification of Differential Equations by Dynamics-Guided Weighted Weak Form with Voting
Jiahui Cheng, Sung Ha Kang, Haomin Zhou +1
In the identification of differential equations from data, significant progresses have been made with the weak/integral formulation. In this paper, we explore the direction of find…
Coefficient-to-Basis Network: A Fine-Tunable Operator Learning Framework for Inverse Problems with Adaptive Discretizations and Theoretical Guarantees
Zecheng Zhang, Hao Liu, Wenjing Liao +1
We propose a Coefficient-to-Basis Network (C2BNet), a novel framework for solving inverse problems within the operator learning paradigm. C2BNet efficiently adapts to different dis…