Physics-informed neural networks for data-driven fluid model#2444
Merged
EvertBunschoten merged 71 commits intodevelopfrom Mar 27, 2025
Merged
Physics-informed neural networks for data-driven fluid model#2444EvertBunschoten merged 71 commits intodevelopfrom
EvertBunschoten merged 71 commits intodevelopfrom
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…f flame front initialization
…cs-informed neural networks
pcarruscag
reviewed
Feb 15, 2025
pcarruscag
reviewed
Mar 25, 2025
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LGTM but @joshkellyjak should approve because I expect the way the merge and unmerge with the turbo ramp branch was done is going to create either conflicts or silently revert the work in that branch. |
Contributor
I'm happy to approve this and then update in turbo ramps branch. Still having the same regression test issue and couldn't find a solution. Probably easier to merge this and then fix on my side. |
joshkellyjak
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Mar 26, 2025
raghava-davuluri
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Sep 12, 2025
…#2444) * Addition of option for using physics-informed neural networks (PINN) for running NICFD calculations. * The use of PINN greatly improves the fluid model consistency. * New tutorial describing the set-up process for generating a PINN using SU2 DataMiner.
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Proposed Changes
The data-driven fluid model in SU2 has been upgraded with the ability to use physics-informed neural networks (PINN's) to compute the fluid thermodynamic state for NICFD flows. This functionality was presented during the 2024 SU2 conference and the issues regarding non-physical behavior have been addressed.
The use of PINN makes the data-driven fluid model substantially more robust and efficient for flow and adjoint simulations of flows of fluids in the non-ideal thermodynamic state.
PINN's can be trained using SU2 DataMiner for any fluid or compatible mixture of fluids available in the CoolProp library. An elaborate tutorial integrating SU2 DataMiner with the data-driven fluid solver in SU2 will follow soon!
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