An Efficient Bayesian Framework for Inverse Problems via Optimization and Inversion: Surrogate Modeling, Parameter Inference, and Uncertainty Quantification.

Mihaela Chiappetta

Mihaela Chiappetta Massimo Carraturo Alexander Raßloff Markus Kästner Ferdinando Auricchio An Efficient Bayesian Framework for Inverse Problems via Optimization and Inversion: Surrogate Modeling, Parameter Inference, and Uncertainty Quantification. 2026 February abs/2602.04537 CoRR https://doi.org/10.48550/arXiv.2602.04537 db/journals/corr/corr2602.html#abs-2602-04537 streams/journals/corr

Mihaela Chiappetta Chiara Piazzola Lorenzo Tamellini Alessandro Reali Ferdinando Auricchio Massimo Carraturo Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing. 2023 abs/2311.03823 CoRR https://doi.org/10.48550/arXiv.2311.03823 db/journals/corr/corr2311.html#abs-2311-03823

Mihaela Chiappetta Chiara Piazzola Massimo Carraturo Lorenzo Tamellini Alessandro Reali Ferdinando Auricchio Inverse and forward sparse-grids-based uncertainty quantification analysis of laser-based powder bed fusion of metals. 2022 abs/2210.06839 CoRR https://doi.org/10.48550/arXiv.2210.06839 db/journals/corr/corr2210.html#abs-2210-06839

Ferdinando Auricchio Massimo Carraturo Markus Kästner Chiara Piazzola Alexander Raßloff Alessandro Reali Lorenzo Tamellini