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Employment (1)

Universidad de Costa Rica : San Jose, San José, CR

Employment
Source: Self-asserted source
José Pablo Quesada-Molina

Education and qualifications (4)

Politecnico di Milano: Milano, Lombardia, IT

2020 to present | PhD Candidate. Program of Structural, Seismic and Geotechnical Engineering (SSGE) (Department of Civil and Environmental Engineering)
Qualification
Source: Self-asserted source
José Pablo Quesada-Molina

Politecnico di Milano: Milano, Lombardia, IT

2018 to 2020 | Master of Science (Materials Engineering and Nanotechnology)
Education
Source: Self-asserted source
José Pablo Quesada-Molina

Universidad de Costa Rica: San Jose, San José, CR

2013 to 2015 | Licentiate Degree (Mechanical Engineering)
Education
Source: Self-asserted source
José Pablo Quesada-Molina

Universidad de Costa Rica: San Jose, San José, CR

2009 to 2014 | Bachelor Degree (Mechanical Engineering)
Education
Source: Self-asserted source
José Pablo Quesada-Molina

Works (9)

Uncertainty Quantification at the Microscale: A Data-Driven Multi-Scale Approach

2022-11-01 | Conference paper
DOI: 10.3390/ecsa-9-13351
Contributors: José Pablo Quesada-Molina; Stefano Mariani
Source: check_circle
Crossref

Deep Learning-based Multiscale Modelling of Polysilicon MEMS

2022 23rd International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)
2022-04-25 | Conference paper
DOI: 10.1109/eurosime54907.2022.9758899
Source: Self-asserted source
José Pablo Quesada-Molina

Hybrid Model-Based and Data-Driven Solution for Uncertainty Quantification at the Microscale

Micro and Nanosystems
2022-03-28 | Journal article
DOI: 10.2174/1876402914666220328123601
Part of ISSN: 1876-4029
Source: Self-asserted source
José Pablo Quesada-Molina

A Deep Learning Approach for Polycrystalline Microstructure-Statistical Property Prediction

2021 | Book chapter
DOI: 10.1007/978-3-030-77977-1_44
Contributors: José Pablo Quesada-Molina; Stefano Mariani
Source: check_circle
Crossref

A Two-Scale Multi-Physics Deep Learning Model for Smart MEMS Sensors

Journal of Materials Science and Chemical Engineering
2021 | Journal article
DOI: 10.4236/msce.2021.98004
Part of ISSN: 2327-6045
Part of ISSN: 2327-6053
Source: Self-asserted source
José Pablo Quesada-Molina

Two-Scale Deep Learning Model for Polysilicon MEMS Sensors

2021-09-22 | Conference paper
DOI: 10.3390/IOCA2021-10888
Contributors: José Pablo Quesada-Molina; Stefano Mariani
Source: check_circle
Crossref

A Deep Learning-Based Approach to Uncertainty Quantification for Polysilicon MEMS

Engineering Proceedings
2021-04-14 | Journal article
DOI: 10.3390/micromachines2021-09556
Part of ISSN: 2673-4591
Source: Self-asserted source
José Pablo Quesada-Molina
grade
Preferred source (of 2)‎

Mechanical Characterization of Polysilicon MEMS Devices: a Stochastic, Deep Learning-based Approach

2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE)
2020-07 | Conference paper
DOI: 10.1109/eurosime48426.2020.9152690
ISBN: 9781728160498
Source: Self-asserted source
José Pablo Quesada-Molina

Stochastic Mechanical Characterization of Polysilicon MEMS: A Deep Learning Approach

Proceedings
2019-11-14 | Journal article
DOI: 10.3390/ecsa-6-06574
Part of ISSN: 2504-3900
Source: Self-asserted source
José Pablo Quesada-Molina
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