no code implementations • 31 Oct 2023 • Marcus Haywood-Alexander, Wei Liu, Kiran Bacsa, Zhilu Lai, Eleni Chatzi
The intersection of physics and machine learning has given rise to a paradigm that we refer to here as physics-enhanced machine learning (PEML), aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods.
1 code implementation • 9 Oct 2022 • Wei Liu, Zhilu Lai, Kiran Bacsa, Eleni Chatzi
Typically, conventional variational inference models are parameterized by neural networks independent of the latent dynamics models.
1 code implementation • 16 Jul 2022 • Zhilu Lai, Wei Liu, Xudong Jian, Kiran Bacsa, Limin Sun, Eleni Chatzi
In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems.
1 code implementation • 16 Oct 2021 • Wei Liu, Zhilu Lai, Kiran Bacsa, Eleni Chatzi
To address this, we bridge physics-based state space models with Deep Markov Models, thus delivering a hybrid modeling framework for unsupervised learning and identification of nonlinear dynamical systems.