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portfolio
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publications
rvesimulator: An Automated Representative Volume Element Simulator for Data-Driven Material Discovery
Published in AI4Mat Workshop (NeurIPS 2023), 2023
rvesimulator is a Python-Abaqus framework for automating RVE simulations to support large-scale material data generation.
Recommended citation: Yi, J. & Bessa, M. (2023). “rvesimulator: An automated representative volume element simulator for data-driven material discovery.” AI4Mat 2023 Workshop, NeurIPS.
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Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits
Published in Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24), 2024
This paper introduces mixed Pareto-lexicographic orders for multi-objective stochastic linear bandits, allowing algorithms to capture both Pareto and lexicographic preferences via the Grossone methodology.
Recommended citation: Cheng, J., Xue, B., Yi, J., & Zhang, Q. (2024). “Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits.” *Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24)*, pp. 11489–11497.
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Practical Multi-fidelity Machine Learning: Fusion of Deterministic and Bayesian Models
Published in arXiv preprint, 2024
A practical multi-fidelity strategy for problems spanning low- and high-dimensional domains, integrating a non-probabilistic regression model for the low-fidelity with a Bayesian model for the high-fidelity.
Recommended citation: Yi, J., Cheng, J., & Bessa, M. (2024). “Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models.” arXiv preprint arXiv:2407.15110.
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Cooperative Bayesian and Variance Networks Disentangle Aleatoric and Epistemic Uncertainties
Published in arXiv preprint, 2025
A simple yet effective cooperative training strategy that integrates a Variance estimation network with a Bayesian neural network, achieving accurate mean prediction while disentangling aleatoric and epistemic uncertainties.
Recommended citation: Yi, J. & Bessa, M. (2025). “Cooperative Bayesian and Variance Networks Disentangle Aleatoric and Epistemic Uncertainties.” arXiv preprint arXiv:2505.02743.
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Single-to-multi-fidelity history-dependent learning with uncertainty quantification and disentanglement
Published in arXiv preprint, 2025
This work generalizes data-driven learning to history‐dependent multi-fidelity settings, enabling uncertainty quantification and disentanglement of model vs noise.
Recommended citation: Yi, J., Ferreira, B. P., & Bessa, M. A. (2025). “Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement.” arXiv preprint arXiv:2507.13416.
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talks
Learning Uncertainty-Aware Composite Constitutive Laws via Bayesian Recurrent Neural Network
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
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