output
20172026
most citedPhysics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty

63 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

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Showing cs.LGShow all

3 papers · 1 filter

cs.LG2022★ 39 cited

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…

cs.LG2022★ 34 cited

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…

cs.LG2021★ 63 cited

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…