| Deep convolutional neural network-based structural damage localization and quantification using transmissibility data S Cofre-Martel, P Kobrich, E Lopez Droguett, V Meruane Shock and Vibration 2019, 2019 | 77 | 2019 |
| Integration of deep learning and Bayesian networks for condition and operation risk monitoring of complex engineering systems R Moradi, S Cofre-Martel, EL Droguett, M Modarres, KM Groth Reliability Engineering & System Safety 222, 108433, 2022 | 47 | 2022 |
| Big machinery data preprocessing methodology for data-driven models in prognostics and health management S Cofre-Martel, E Lopez Droguett, M Modarres Sensors 21 (20), 6841, 2021 | 27 | 2021 |
| Remaining useful life estimation through deep learning partial differential equation models: A framework for degradation dynamics interpretation using latent variables S Cofre-Martel, E Lopez Droguett, M Modarres Shock and Vibration 2021, 1-15, 2021 | 20 | 2021 |
| Exploring Quantum Machine Learning and feature reduction techniques for wind turbine pitch fault detection C Correa-Jullian, S Cofre-Martel, G San Martin, E Lopez Droguett, ... Energies 15 (8), 2792, 2022 | 13 | 2022 |
| Neural Network and Particle Filtering: A Hybrid Framework for Crack Propagation Prediction SF Karimian, R Moradi, S Cofre-Martel, KM Groth, M Modarres Virtual: 3rd International Symposium on Structural Health Monitoring and …, 2020 | 13 | 2020 |
| Transmissibility based structural assessment using deep convolutional neural network S Cofré, P Kobrich, EL Droguett, V Meruane Proc. ISMA, 2018 | 7 | 2018 |
| Uncovering the underlying physics of degrading system behavior through a deep neural network framework: The case of remaining useful life prognosis S Cofre-Martel, EL Droguett, M Modarres arXiv preprint arXiv:2006.09288, 2020 | 4 | 2020 |
| Defining degradation states for diagnosis classification models in real systems based on monitoring data S Cofre-Martel, C Correa-Jullian, E López Droguett, KM Groth, ... Proceedings of the 31st European Safety and Reliability Conference (ESREL …, 2021 | 3 | 2021 |
| Uncovering the Underlying Physics of Degrading System Behavior through a Deep Neural Network Framework: The Case of Rul Prognosis S Cofre-Martel, EL Droguett, M Modarres | 2 | 2021 |
| A deep learning based framework for physical assets' health prognostics under uncertainty for big Machinery Data SMI Cofré Martel Universidad de Chile, 2018 | 1 | 2018 |
| Physics-Informed Neural Networks for Remaining Useful Life Estimation of a Vapor Recovery Unit System S Cofré-Martel, EL Droguett, M Modarres PSAM 16, 2022 | | 2022 |
| A Physics-Informed Neural Network Framework for Big Machinery Data in Prognostics and Health Management for Complex Engineering Systems SMIC Martel University of Maryland, College Park, 2022 | | 2022 |
| Research Article Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent … S Cofre-Martel, EL Droguett, M Modarres | | 2021 |
| A physics-informed deep learning approach for fatigue crack propagation S Cofre-Martel, EL Droguett, M Modarres Research Publishing, Singapore, 2020 | | 2020 |
| Modelo de detección de fallas y faltas para sistema neumático de turbinas de aviones Boeing 767 a través de machine learning SMI Cofré Martel Universidad de Chile, 2017 | | 2017 |
| A Bayesian Method for Estimating Potential Impact of Increase in STI on Component Failure Rates S Cofre-Martela, C Rochonb, A Mosleha, EL Droguetta, J Hillerc | | |
Enrique Lopez DroguettUniversity of California, Los AngelesVerified email at ucla.edu
