Search Results for author: Kiran Bacsa

Found 4 papers, 3 papers with code

Discussing the Spectra of Physics-Enhanced Machine Learning via a Survey on Structural Mechanics Applications

no code implementations31 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.

Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems

1 code implementation9 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.

Variational Inference

Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

1 code implementation16 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.

Physics-informed machine learning

Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty

1 code implementation16 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.

Variational Inference

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