A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators

Maximillian Fornitz Vording, Peter O. Okeyo, Juan José Rubio Guillamón, Mikkel Nørgaard Schmidt, Peter Emil Larsen, Tommy Sonne Alstrøm

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Abstract

Thermal analysis using resonating micro-electromechanical systems shows great promise in characterizing materials in the early stages of research. Through thermal cycles and actuation using a piezoelectric speaker, the resonant behaviour of a model drug, theophylline monohydrate, is measured across the surface whilst using a laser-Doppler vibrometer for readout. Acquired is a sequence of spectra that are strongly correlated in time, temperature and spatial location of the readout.
Traditionally, each spectrum is analyzed individually to locate the resonance peak. We propose a Bayesian model using a warped Gaussian process prior taking the correlations into account and demonstrate on both synthetic and experimental data, that it yields better estimates of both location and amplitude of the resonance peak. Thus, the proposed model can give a more precise characterization of drugs, which is important in drug discovery and development
Original languageEnglish
Title of host publicationProceedings of 2019 IEEE International Workshop on Machine Learning for Signal Processing
Number of pages6
PublisherIEEE
Publication date2019
ISBN (Print)978-1-7281-0824-7
DOIs
Publication statusPublished - 2019
Event2019 IEEE International Workshop on Machine Learning for Signal Processing - University of Pittsburgh, Pittsburgh, United States
Duration: 13 Oct 201916 Oct 2019

Conference

Conference2019 IEEE International Workshop on Machine Learning for Signal Processing
LocationUniversity of Pittsburgh
CountryUnited States
CityPittsburgh
Period13/10/201916/10/2019

Keywords

  • Bayesian learning and modeling
  • Gaussian processes
  • Drug characterisation
  • Thermomechanical analysis

Cite this

Vording, M. F., Okeyo, P. O., Rubio Guillamón, J. J., Schmidt, M. N., Larsen, P. E., & Alstrøm, T. S. (2019). A Bayesian Generative Model With Gaussian Process Priors For Thermomechanical Analysis Of Micro-Resonators. In Proceedings of 2019 IEEE International Workshop on Machine Learning for Signal Processing IEEE. https://doi.org/10.1109/MLSP.2019.8918876