Bi-fidelity Modeling of Uncertain and Partially Unknown Systems using DeepONets. (arXiv:2204.00997v2 [stat.ML] UPDATED)

Recent advances in modeling large-scale complex physical systems have shifted
research focuses towards data-driven techniques. However, generating datasets
by simulating complex systems can require significant computational resources.
Similarly, acquiring experimental datasets can prove difficult as well. For
these systems, often computationally inexpensive, but in general inaccurate,
models, known as the low-fidelity models, are available. In this paper, we
propose a bi-fidelity modeling approach for complex physical systems, where we
model the discrepancy between the true system's response and low-fidelity
response …

arxiv bi fidelity ml modeling systems uncertain