63 citations · 157 across the 8 of their papers we have counts for
6 papers · 1 filter
Causal Convolutional Neural Networks as Finite Impulse Response Filters
Kiran Bacsa, Wei Liu, Xudong Jian +2
This study investigates the behavior of Causal Convolutional Neural Networks (CNNs) with quasi-linear activation functions when applied to time-series data characterized by multimo…
Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
Wei Liu, Kiran Bacsa, Loon Ching Tang +1
Understanding and modeling nonlinear dynamical systems is a fundamental challenge across science and engineering. Deep learning has shown remarkable potential for capturing complex…
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…