Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Kiran Bacsa"'
Publikováno v:
Data-Centric Engineering, Vol 5 (2024)
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 individual shortcomings of data- or physics-only methods. In this paper, the s
Externí odkaz:
https://doaj.org/article/6a7083e20b30419a8a358b9f7669cf1c
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-26 (2023)
Abstract We propose a new variational autoencoder (VAE) with physical constraints capable of learning the dynamics of Multiple Degree of Freedom (MDOF) dynamic systems. Standard variational autoencoders place greater emphasis on compression than inte
Externí odkaz:
https://doaj.org/article/69125f6014294d8bb0d91244e71d3c62
Publikováno v:
Data-Centric Engineering, Vol 3 (2022)
The 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 particularly true for structural systems, which are typically high-dimensional i
Externí odkaz:
https://doaj.org/article/1d9538089d3747aa85fb88867ea8f5e2
Publikováno v:
Scientific Reports, 13 (1)
We propose a new variational autoencoder (VAE) with physical constraints capable of learning the dynamics of Multiple Degree of Freedom (MDOF) dynamic systems. Standard variational autoencoders place greater emphasis on compression than interpretabil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b20616ac228168f6ded6d13c129379a
https://doi.org/10.21203/rs.3.rs-2115007/v1
https://doi.org/10.21203/rs.3.rs-2115007/v1
Publikováno v:
Mechanical Systems and Signal Processing. 178:109276
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 latent structure of nonlinear dynamical systems from measurement data,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb8c6df634d01e50cddd8f806384ee41
https://doi.org/10.1016/j.ymssp.2022.109276
https://doi.org/10.1016/j.ymssp.2022.109276