2023

Carlon, A. G., de Carvalho Dantas Maia, C. D., Lopez, R. H., Torii, A. J., & Miguel, L. F. F. (2023). A polynomial chaos efficient global optimization approach for Bayesian optimal experimental design. Probabilistic Engineering Mechanics, 72, 103454. https://doi.org/10.1016/j.probengmech.2023.103454
Bartuska, A., Carlon, A. G., Espath, L., Krumscheid, S., & Tempone, R. (2023). Double-loop quasi-Monte Carlo estimator for nested integration (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2302.14119

2022

Carlon, A. G., Kroetz, H. M., Torii, A. J., Lopez, R. H., & Miguel, L. F. F. (2022). Risk optimization using the Chernoff bound and stochastic gradient descent. Reliability Engineering & System Safety, 223, 108512. https://doi.org/10.1016/j.ress.2022.108512
Carlon, A., Espath, L., & Tempone, R. (2022). Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization. arXiv. https://doi.org/10.48550/ARXIV.2208.00441

2020

Carlon, A. G., Torii, A. J., Lopez, R. H., & de Cursi, J. E. S. (2020). Stochastic Gradient Descent for Risk Optimization. Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling, 424–435. https://doi.org/10.1007/978-3-030-53669-5_31
Carlon, A. G., Dia, B. M., Espath, L., Lopez, R. H., & Tempone, R. (2020). Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization. Computer Methods in Applied Mechanics and Engineering, 363, 112909. https://doi.org/10.1016/j.cma.2020.112909
Carlon, A., Espath, L., Lopez, R., & Tempone, R. (2020). Multi-Iteration Stochastic Optimizers. arXiv. https://doi.org/10.48550/ARXIV.2011.01718

2019

Carlon, A. G., Lopez, R. H., Espath, L. F. R., Miguel, L. F. F., & Beck, A. T. (2019). A stochastic gradient approach for the reliability maximization of passively controlled structures. Engineering Structures, 186, 1–12. https://doi.org/10.1016/j.engstruct.2019.01.121

2018

Carlon, A. G., Dia, B. M., Espath, L. F. R., Lopez, R. H., & Tempone, R. (2018). Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization. arXiv. https://doi.org/10.48550/ARXIV.1807.00653