| Multiple surrogates: how cross-validation errors can help us to obtain the best predictor FAC Viana, RT Haftka, V Steffen Structural and Multidisciplinary Optimization 39 (4), 439-457, 2009 | 488 | 2009 |
| Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? FAC Viana, TW Simpson, V Balabanov, V Toropov AIAA Journal 52 (4), 670-690, 2014 | 445* | 2014 |
| Design and analysis of computer experiments in multidisciplinary design optimization: a review of how far we have come or not TW Simpson, V Toropov, V Balabanov, FAC Viana 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA …, 2008 | 423 | 2008 |
| An algorithm for fast optimal Latin hypercube design of experiments FAC Viana, G Venter, V Balabanov International Journal for Numerical Methods in Engineering 82 (2), 135-156, 2010 | 405 | 2010 |
| Efficient global optimization algorithm assisted by multiple surrogate techniques FAC Viana, RT Haftka, LT Watson Journal of Global Optimization 56 (2), 669-689, 2013 | 302 | 2013 |
| A Tutorial on Latin Hypercube Design of Experiments FAC Viana Quality and Reliability Engineering International, 2015 | 246 | 2015 |
| Things you wanted to know about the Latin hypercube design and were afraid to ask FAC Viana 10th World Congress on Structural and Multidisciplinary Optimization …, 2013 | 154 | 2013 |
| SURROGATES toolbox user’s guide FAC Viana Users manual, http://fchegury. googlepages. com, 2009 | 148* | 2009 |
| Hybrid physics-informed neural networks for lithium-ion battery modeling and prognosis RG Nascimento, M Corbetta, CS Kulkarni, FAC Viana Journal of Power Sources 513, 230526, 2021 | 140 | 2021 |
| Multimodal vibration damping through piezoelectric patches and optimal resonant shunt circuits FAC Viana, V Steffen Jr Journal of the Brazilian Society of Mechanical Sciences and Engineering 28 …, 2006 | 134 | 2006 |
| A tutorial on solving ordinary differential equations using Python and hybrid physics-informed neural network RG Nascimento, K Fricke, FAC Viana Engineering Applications of Artificial Intelligence 96, DOI: 10.1016/j …, 2020 | 126 | 2020 |
| Making the most out of surrogate models: tricks of the trade FAC Viana, C Gogu, RT Haftka ASME 2010 International Design Engineering Technical Conferences & Computers …, 2010 | 120 | 2010 |
| Physics-informed neural networks for missing physics estimation in cumulative damage models: a case study in corrosion fatigue A Dourado, FAC Viana ASME Journal of Computing and Information Science in Engineering, 2020 | 118 | 2020 |
| A physics-informed neural network for wind turbine main bearing fatigue YA Yucesan, FAC Viana International Journal of Prognostics and Health Management 11 (1), 2020 | 108 | 2020 |
| Estimating model inadequacy in ordinary differential equations with physics-informed neural networks FAC Viana, RG Nascimento, A Dourado, YA Yucesan Computers and Structures 245, 106458 (DOI: 10.1016/j.compstruc.2020.10, 2021 | 80 | 2021 |
| Tuning dynamic vibration absorbers by using ant colony optimization FAC Viana, GI Kotinda, DA Rade, V Steffen Jr Computers & Structures 86 (13-14), 1539-1549, 2008 | 80 | 2008 |
| Surrogate-based optimization with parallel simulations using the probability of improvement FAC Viana, RT Haftka 13th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, AIAA …, 2010 | 79 | 2010 |
| A survey of modeling for prognosis and health management of industrial equipment YA Yucesan, A Dourado, FAC Viana Advanced Engineering Informatics 50, 101404, 2021 | 78 | 2021 |
| Fleet Prognosis with Physics-informed Recurrent Neural Networks RG Nascimento, FAC Viana The 12th International Workshop on Structural Health Monitoring, 2019 | 75 | 2019 |
| A hybrid physics-informed neural network for main bearing fatigue prognosis under grease quality variation YA Yucesan, FAC Viana Mechanical Systems and Signal Processing 171, 108875, 2022 | 73 | 2022 |
Raphael HaftkaUniversity of Florida在 ufl.edu 的电子邮件经过验证