Miguel A. Bessa

According to our database, Miguel A. Bessa authored at least 21 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling.

Jiaxiang Yi, Bernardo P. Ferreira, Miguel A. Bessa

CoRR, July, 2025

Continual learning for surface defect segmentation by subnetwork creation and selection.

Aleksandr Dekhovich, Miguel A. Bessa

J. Intell. Manuf., June, 2025

Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency.

Bernardo P. Ferreira, Miguel A. Bessa

CoRR, May, 2025

Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties.

Jiaxiang Yi, Miguel A. Bessa

CoRR, May, 2025

iPINNs: incremental learning for Physics-informed neural networks.

Aleksandr Dekhovich, Marcel H. F. Sluiter, David M. J. Tax, Miguel A. Bessa

Eng. Comput., February, 2025

Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning.

Igor Kuszczak, Gawel I. Kus, Federico Bosi, Miguel A. Bessa

CoRR, February, 2025

2024

Neural network relief: a pruning algorithm based on neural activity.

Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter, Miguel A. Bessa

Mach. Learn., May, 2024

f3dasm: Framework for Data-Driven Design and Analysis of Structures and Materials.

M. P. van der Schelling, Bernardo P. Ferreira, Miguel A. Bessa

J. Open Source Softw., 2024

Multi-objective Bayesian Optimisation of Spinodoid Cellular Structures for Crush Energy Absorption.

Hirak Kansara, Siamak Farajzadeh Khosroshahi, Leo Guo, Miguel A. Bessa, Wei Tan

CoRR, 2024

Consistent machine learning for topology optimization with microstructure-dependent neural network material models.

Harikrishnan Vijayakumaran, Jonathan B. Russ, Glaucio H. Paulino, Miguel A. Bessa

CoRR, 2024

Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models.

Jiaxiang Yi, Ji Cheng, Miguel A. Bessa

CoRR, 2024

Neural topology optimization: the good, the bad, and the ugly.

Suryanarayanan Manoj Sanu, Alejandro M. Aragon, Miguel A. Bessa

CoRR, 2024

Engineering software 2.0 by interpolating neural networks: unifying training, solving, and calibration.

Chanwook Park, Sourav Saha, Jiachen Guo, Xiaoyu Xie, Satyajit Mojumder, Miguel A. Bessa, Dong Qian, Wei Chen, Gregory J. Wagner, Jian Cao, Wing Kam Liu

CoRR, 2024

Gradient-free neural topology optimization.

Gawel I. Kus, Miguel A. Bessa

CoRR, 2024

2023

Continual prune-and-select: class-incremental learning with specialized subnetworks.

Aleksandr Dekhovich, David M. J. Tax, Marcel H. F. Sluiter, Miguel A. Bessa

Appl. Intell., July, 2023

CRATE: A Python package to perform fast material simulations.

Bernardo P. Ferreira, F. M. Andrade Pires, Miguel A. Bessa

J. Open Source Softw., 2023

2022

Cooperative data-driven modeling.

Aleksandr Dekhovich, O. Taylan Turan, Jiaxiang Yi, Miguel A. Bessa

CoRR, 2022

2021

Sparse quantum Gaussian processes to counter the curse of dimensionality.

Gawel I. Kus, Sybrand van der Zwaag, Miguel A. Bessa

Quantum Mach. Intell., 2021

Adaptive Clustering-based Reduced-Order Modeling Framework: Fast and accurate modeling of localized history-dependent phenomena.

Bernardo P. Ferreira, F. M. Andrade Pires, Miguel A. Bessa

CoRR, 2021

Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning.

Dongil Shin, Andrea Cupertino, Matthijs H. J. de Jong, Peter G. Steeneken, Miguel A. Bessa, Richard A. Norte

CoRR, 2021

A New Automated Energy Meter Fraud Detection System Based on Artificial Intelligence.

João Pedro Klock, Jhonatan Corrêa, Miguel A. Bessa, Janier Arias-Garcia, Felipe Barboza, Carmo Meinertz

Proceedings of the XI Brazilian Symposium on Computing Systems Engineering, 2021