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cs.CE2026
An Efficient Bayesian Framework for Inverse Problems via Optimization and Inversion: Surrogate Modeling, Parameter Inference, and Uncertainty Quantification
Mihaela Chiappetta, Massimo Carraturo, Alexander RaÃloff +2
The present paper proposes a Bayesian framework for inverse problems that seamlessly integrates optimization and inversion to enable rapid surrogate modeling, accurate parameter in…
cs.CE2024
Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing
Mihaela Chiappetta, Chiara Piazzola, Lorenzo Tamellini +3
We present an efficient approach to quantify the uncertainties associated with the numerical simulations of the laser-based powder bed fusion of metals processes. Our study focuses…