Thiago Rios

Orcid: 0000-0003-1708-3378

According to our database1, Thiago Rios authored at least 13 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Large Language and Text-to-3D Models for Engineering Design Optimization.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

2022
Multitask Shape Optimization Using a 3-D Point Cloud Autoencoder as Unified Representation.
IEEE Trans. Evol. Comput., 2022

2021
Exploiting Generative Models for Performance Predictions of 3D Car Designs.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Exploiting Local Geometric Features in Vehicle Design Optimization with 3D Point Cloud Autoencoders.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021

Point2FFD: Learning Shape Representations of Simulation-Ready 3D Models for Engineering Design Optimization.
Proceedings of the International Conference on 3D Vision, 2021

2020
On the Performance of Oversampling Techniques for Class Imbalance Problems.
Dataset, May, 2020

Back To Meshes: Optimal Simulation-ready Mesh Prototypes For Autoencoder-based 3D Car Point Clouds.
Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, 2020

On the Performance of Oversampling Techniques for Class Imbalance Problems.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2020

Feature Visualization for 3D Point Cloud Autoencoders.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019
Optimal Evolutionary Optimization Hyper-parameters to Mimic Human User Behavior.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

Scalability of Learning Tasks on 3D CAE Models Using Point Cloud Autoencoders.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

On the Efficiency of a Point Cloud Autoencoder as a Geometric Representation for Shape Optimization.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2019

Learning Time-Series Data of Industrial Design Optimization using Recurrent Neural Networks.
Proceedings of the 2019 International Conference on Data Mining Workshops, 2019


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