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 | 466 | 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 | 422* | 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 | 406 | 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 | 387 | 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 | 292 | 2013 |
A Tutorial on Latin Hypercube Design of Experiments FAC Viana Quality and Reliability Engineering International, 2015 | 212 | 2015 |
SURROGATES toolbox user’s guide FAC Viana Users manual, http://fchegury. googlepages. com, 2009 | 146* | 2009 |
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 | 141 | 2013 |
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 | 127 | 2006 |
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 | 115 | 2010 |
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 | 101 | 2020 |
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 | 94 | 2021 |
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 | 92 | 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 | 92 | 2020 |
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 | 76 | 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 | 75 | 2010 |
Fleet Prognosis with Physics-informed Recurrent Neural Networks RG Nascimento, FAC Viana The 12th International Workshop on Structural Health Monitoring, 2019 | 65 | 2019 |
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 | 60 | 2021 |
Why not run the efficient global optimization algorithm with multiple surrogates? FAC Viana, RT Haftka, LT Watson 51st AIAAASMEASCEAHSASC Structures Structural Dynamics and Materials …, 2010 | 60 | 2010 |
Using multiple surrogates for metamodeling FAC Viana, RT Haftka 7th ASMO-UK/ISSMO International Conference on Engineering Design Optimization, 2008 | 60 | 2008 |