Ensemble models outperform single model uncertainties and predictions
for operator-learning of hypersonic flows
- URL: http://arxiv.org/abs/2311.00060v2
- Date: Fri, 3 Nov 2023 13:43:28 GMT
- Title: Ensemble models outperform single model uncertainties and predictions
for operator-learning of hypersonic flows
- Authors: Victor J. Leon, Noah Ford, Honest Mrema, Jeffrey Gilbert, Alexander
New
- Abstract summary: Training scientific machine learning (SciML) models on limited high-fidelity data offers one approach to rapidly predict behaviors for situations that have not been seen before.
High-fidelity data is itself in limited quantity to validate all outputs of the SciML model in unexplored input space.
We extend a DeepONet using three different uncertainty mechanisms: mean-variance estimation, evidential uncertainty, and ensembling.
- Score: 43.148818844265236
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: High-fidelity computational simulations and physical experiments of
hypersonic flows are resource intensive. Training scientific machine learning
(SciML) models on limited high-fidelity data offers one approach to rapidly
predict behaviors for situations that have not been seen before. However,
high-fidelity data is itself in limited quantity to validate all outputs of the
SciML model in unexplored input space. As such, an uncertainty-aware SciML
model is desired. The SciML model's output uncertainties could then be used to
assess the reliability and confidence of the model's predictions. In this
study, we extend a DeepONet using three different uncertainty quantification
mechanisms: mean-variance estimation, evidential uncertainty, and ensembling.
The uncertainty aware DeepONet models are trained and evaluated on the
hypersonic flow around a blunt cone object with data generated via
computational fluid dynamics over a wide range of Mach numbers and altitudes.
We find that ensembling outperforms the other two uncertainty models in terms
of minimizing error and calibrating uncertainty in both interpolative and
extrapolative regimes.
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