63 citations · 140 across the 5 of their papers we have counts for
5 papers
On the Rate of Convergence of Kolmogorov-Arnold Network Regression Estimators
Wei Liu, Eleni Chatzi, Zhilu Lai
Kolmogorov-Arnold Networks (KANs) approximate multivariate functions by composing univariate transformations through additive or multiplicative aggregation. We establish convergenc…
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications
Marcus Haywood-Alexander, Wei Liu, Kiran Bacsa +2
The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individu…
Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems
Wei Liu, Zhilu Lai, Kiran Bacsa +1
Accurate structural response prediction forms a main driver for structural health monitoring and control applications. This often requires the proposed model to adequately capture…
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures
Zhilu Lai, Wei Liu, Xudong Jian +3
The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is part…
Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty
Wei Liu, Zhilu Lai, Kiran Bacsa +1
In this paper, we propose a probabilistic physics-guided framework, termed Physics-guided Deep Markov Model (PgDMM). The framework targets the inference of the characteristics and…