6 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
Graph Laplacian-based Bayesian Multi-fidelity Modeling
Orazio Pinti, Jeremy M. Budd, Franca Hoffmann +1
We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Lapl…
cs.LG2023
A few-shot graph Laplacian-based approach for improving the accuracy of low-fidelity data
Orazio Pinti, Assad A. Oberai
Low-fidelity data is typically inexpensive to generate but inaccurate. On the other hand, high-fidelity data is accurate but expensive to obtain. Multi-fidelity methods use a small…
cs.LG2023★ 6 cited
Deep Learning and Computational Physics (Lecture Notes)
Deep Ray, Orazio Pinti, Assad A. Oberai
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California. They should be accessible to a typical engineering gradua…