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 (SBESC, 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)

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)

Sparse quantum Gaussian processes to counter the curse of dimensionality. - Gawel I. Kus; Sybrand van der Zwaag; Miguel A. Bessa (Quantum Mach. Intell., 2021)

Cooperative data-driven modeling. - Aleksandr Dekhovich; O. Taylan Turan; Jiaxiang Yi; Miguel A. Bessa (CoRR, 2022)

CRATE: A Python package to perform fast material simulations. - Bernardo P. Ferreira; F. M. Andrade Pires; Miguel A. Bessa (J. Open Source Softw., 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., 2023)

Gradient-free neural topology optimization. - Gawel I. Kus; 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 041; Gregory J. Wagner; Jian Cao; Wing Kam Liu (CoRR, 2024)

Neural topology optimization: the good, the bad, and the ugly. - Suryanarayanan Manoj Sanu; Alejandro M. Aragon; Miguel A. Bessa (CoRR, 2024)

Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models. - Jiaxiang Yi; Ji Cheng 001; Miguel A. Bessa (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)

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)

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)

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., 2024)

Meta-neural Topology Optimization: Knowledge Infusion with Meta-learning. - Igor Kuszczak; Gawel I. Kus; Federico Bosi; Miguel A. Bessa (CoRR, 2025)

iPINNs: incremental learning for Physics-informed neural networks. - Aleksandr Dekhovich; Marcel H. F. Sluiter; David M. J. Tax; Miguel A. Bessa (Eng. Comput., 2025)

Cooperative Bayesian and variance networks disentangle aleatoric and epistemic uncertainties. - Jiaxiang Yi; Miguel A. Bessa (CoRR, 2025)

Automatically Differentiable Model Updating (ADiMU): conventional, hybrid, and neural network material model discovery including history-dependency. - Bernardo P. Ferreira; Miguel A. Bessa (CoRR, 2025)

Continual learning for surface defect segmentation by subnetwork creation and selection. - Aleksandr Dekhovich; Miguel A. Bessa (J. Intell. Manuf., 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, 2025)