Felipe A. C. Viana

发表

A tutorial on solving ordinary differential equations using Python and hybrid physics-informed neural network

Renato Giorgiani do Nascimento, Kajetan Fricke, Felipe A. C. Viana, 2020, Eng. Appl. Artif. Intell..

Developing a Probabilistic Load Spectrum for Fatigue Modeling

Liping Wang, You Ling, Isaac Asher, 2017 .

Sequential sampling for contour estimation with concurrent function evaluations

Layne T. Watson, Raphael T. Haftka, Felipe A. C. Viana, 2012 .

Optimization of aircraft structural components by using nature-inspired algorithms and multi-fidelity approximations

Valder Steffen, Sergio Butkewitsch, Marcus de Freitas Leal, 2009, J. Glob. Optim..

Using Cross Validation to Design Conservative Surrogates

Victor Picheny, Raphael T. Haftka, Felipe A. C. Viana, 2010 .

Multiple surrogates for prediction and optimization

Felipe A. C. Viana, F. A. Viana, 2011 .

Conservative Prediction via Safety Margin: Design Through Cross-Validation and Benefits of Multiple Surrogates

Victor Picheny, Raphael T. Haftka, Felipe A. C. Viana, 2009, DAC 2009.

High-Order Integration of Fatigue Crack Growth Using Surrogate Model

Nam-Ho Kim, Felipe A. C. Viana, Matthew J. Pais, 2011 .

Quadcopter Control Optimization through Machine Learning

Renato Giorgiani do Nascimento, Kajetan Fricke, Felipe A. C. Viana, 2020, AIAA Scitech 2020 Forum.

Satellite Image Classification and Segmentation with Transfer Learning

Renato Giorgiani do Nascimento, Felipe A. C. Viana, 2020 .

Fleet Prognosis with Physics-informed Recurrent Neural Networks pdf

Felipe A. C. Viana, Renato Giorgiani Nascimento, F. Viana, 2019, Structural Health Monitoring 2019.

Differential Evolution Applied to the Design of a Three-Dimensional Vehicular Structure

Valder Steffen, Felipe A. C. Viana, Fernando Ce´sar Gama de Oliveira, 2007, DAC 2007.

Efficient Global Optimization with Adaptive Target for Probability of Targeted Improvement

Anirban Chaudhuri, Raphael T. Haftka, Felipe A. C. Viana, 2012 .

Making the Most Out of Surrogate Models: Tricks of the Trade

Raphael T. Haftka, Felipe A. C. Viana, Christian Gogu, 2010, DAC 2010.

Information gain-based inspection scheduling for fatigued aircraft components

You Ling, Isaac Asher, Liping Wang, 2017 .

Identification of a Non-Linear Landing Gear Model Using Nature-Inspired Optimization

Felipe A. C. Viana, Luiz Carlos Sandoval Góes, Valder Steffen, 2008 .

Fatigue Crack Growth Life Assessment for Industrial Applications using Re-meshing and Bayesian Hybrid Techniques

Felipe A. C. Viana, Balajee Ananthasayanam, Arun K. Subramaniyan, 2016 .

Control-Oriented Design Using H-infinity Synthesis and Multiple Surrogates

Raphael T. Haftka, Rick Lind, Felipe A. C. Viana, 2010 .

Visualizing Corrosion in Automobiles using Generative Adversarial Networks

Felipe A. C. Viana, Andre Von Zuben, Renato G. Nascimento, 2020 .

Estimating Parameters and Discrepancy of Computer Models with Graphs and Neural Networks

Jarir Mahfoud, Felipe A. C. Viana, Andre Von Zuben, 2020, AIAA AVIATION 2020 FORUM.

Efficient Global Optimization with Experimental Data: Revisiting the Paper Helicopter Design

Raphael T. Haftka, Gerhard Venter, Felipe A. C. Viana, 2011 .

Enabling high-order integration of fatigue crack growth with surrogate modeling

Felipe A. C. Viana, Matthew J. Pais, Nam H. Kim, 2012 .

A Tutorial on Latin Hypercube Design of Experiments

Felipe A. C. Viana, F. Viana, 2016, Qual. Reliab. Eng. Int..

Using Multiple Surrogates for Minimization of the RMS Error in Meta-Modeling

Raphael T. Haftka, Felipe A. C. Viana, 2008, DAC 2008.

Efficient global optimization algorithm assisted by multiple surrogate techniques

Layne T. Watson, Raphael T. Haftka, Felipe A. C. Viana, 2012, Journal of Global Optimization.

Design Optimization of a Bendable UAV Wing under Uncertainty

Raphael T. Haftka, Peter Ifju, Felipe A. C. Viana, 2010 .

A Probabilistic Hybrid Model for Main Bearing Fatigue Considering Uncertainty in Grease Quality

Felipe A. C. Viana, Yigit A. Yucesan, F. Viana, 2020, AIAA Scitech 2021 Forum.

Surrogate modeling: tricks that endured the test of time and some recent developments

Felipe A. C. Viana, Christian Gogu, Tushar Goel, 2021, Structural and Multidisciplinary Optimization.

A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue

Felipe A. C. Viana, Yigit A. Yucesan, F. Viana, 2023, International Journal of Prognostics and Health Management.

Surrogate modelling for characterising the performance of a dielectric barrier discharge plasma actuator

Raphael T. Haftka, Wei Shyy, Balaji Jayaraman, 2010 .

Identification of external forces in mechanical systems by using LifeCycle model and stress-stiffening effect

Valder Steffen, Felipe A. C. Viana, Domingos A. Rade, 2007 .

Tuning dynamic vibration absorbers by using ant colony optimization

Giovanni Iamin Kotinda, Valder Steffen, Felipe A. C. Viana, 2008 .

Wind Turbine Main Bearing Fatigue Life Estimation with Physics-informed Neural Networks

Felipe A. C. Viana, Yigit A. Yucesan, F. Viana, 2019, Annual Conference of the PHM Society.

IDENTIFICATION OF EXTERNAL LOADS IN MECHANICAL SYSTEMS THROUGH HEURISTIC-BASED OPTIMIZATION METHODS AND DYNAMIC RESPONSES

Valder Steffen, Felipe A. C. Viana, Domingos A. Rade, 2004 .

Physics-Informed Neural Networks for Corrosion-Fatigue Prognosis

Felipe A. C. Viana, Arinan Dourado, F. Viana, 2019, Annual Conference of the PHM Society.