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Johann Guilleminot

Showing results (1-10 of 5) with videos related to

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Journal of Neural Engineering|February 8, 2022
Uncertainty quantification of TMS simulations considering MRI segmentation errorsHao Zhang, Luis Gomez, Johann Guilleminot
Computer Methods in Applied Mechanics and Engineering|September 23, 2021
Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometriesHao Zhang, Johann Guilleminot, Luis J Gomez
Journal of Neural Engineering|February 16, 2022
Uncertainty quantification of TMS simulations considering MRI segmentation errorsHao Zhang, Luis J Gomez, Johann Guilleminot
Proceedings. Mathematical, Physical, and Engineering Sciences|February 14, 2022
Nematic liquid crystalline elastomers are aeolotropic materialsL Angela Mihai, Haoran Wang, Johann Guilleminot, et al.
The Journal of the Acoustical Society of America|April 24, 2022
Learning acoustic responses from experiments: A multiscale-informed transfer learning approachVan Hai Trinh, Johann Guilleminot, Camille Perrot, et al.
Pageof 1

Showing results (1-10 of 5) with videos related to

Sort By:
Pageof 1
Journal of Neural Engineering|February 8, 2022
Uncertainty quantification of TMS simulations considering MRI segmentation errorsHao Zhang, Luis Gomez, Johann Guilleminot
Computer Methods in Applied Mechanics and Engineering|September 23, 2021
Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometriesHao Zhang, Johann Guilleminot, Luis J Gomez
Journal of Neural Engineering|February 16, 2022
Uncertainty quantification of TMS simulations considering MRI segmentation errorsHao Zhang, Luis J Gomez, Johann Guilleminot
Proceedings. Mathematical, Physical, and Engineering Sciences|February 14, 2022
Nematic liquid crystalline elastomers are aeolotropic materialsL Angela Mihai, Haoran Wang, Johann Guilleminot, et al.
The Journal of the Acoustical Society of America|April 24, 2022
Learning acoustic responses from experiments: A multiscale-informed transfer learning approachVan Hai Trinh, Johann Guilleminot, Camille Perrot, et al.
Pageof 1