3 papers
cs.LG2025
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
Data-driven learning is generalized to consider history-dependent multi-fidelity data, while quantifying epistemic uncertainty and disentangling it from data noise (aleatoric uncer…
cs.LG2025
Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
Jiaxiang Yi, Miguel A. Bessa
Real-world data contains aleatoric uncertainty - irreducible noise arising from imperfect measurements or from incomplete knowledge about the data generation process. Mean-variance…
cs.LG2024
Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models
Jiaxiang Yi, Ji Cheng, Miguel A. Bessa
Multi-fidelity machine learning methods address the accuracy-efficiency trade-off by integrating scarce, resource-intensive high-fidelity data with abundant but less accurate low-f…