Surrogates for empirical parameters in the separated flow boiling model using physics informed neural networks: SFM-PINNs

Logan M. Pirnstill; Benjamin Collea; Cho-Ning Huang; Chirag R. Kharangate

doi:10.1016/j.aitf.2025.100011

journal article Open Access Sep 01, 2025

Surrogates for empirical parameters in the separated flow boiling model using physics informed neural networks: SFM-PINNs

Logan M. Pirnstill

Benjamin Collea Cho-Ning Huang Chirag R. Kharangate

AI Thermal Fluids Vol. 2-3 pp. 100011 · Elsevier BV

View at Publisher Save 10.1016/j.aitf.2025.100011

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References

Details

Published: Sep 01, 2025
Vol/Issue: 2-3
Pages: 100011
License: View

Authors

Funding

Office of Naval Research Award: N00014-22-1-2618

Office of Naval Research Global

Cite This Article

Logan M. Pirnstill, Benjamin Collea, Cho-Ning Huang, et al. (2025). Surrogates for empirical parameters in the separated flow boiling model using physics informed neural networks: SFM-PINNs. AI Thermal Fluids, 2-3, 100011. https://doi.org/10.1016/j.aitf.2025.100011

Surrogates for empirical parameters in the separated flow boiling model using physics informed neural networks: SFM-PINNs

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