Quantifying uncertainty for deep learning based forecasting and
flow-reconstruction using neural architecture search ensembles
- URL: http://arxiv.org/abs/2302.09748v1
- Date: Mon, 20 Feb 2023 03:57:06 GMT
- Title: Quantifying uncertainty for deep learning based forecasting and
flow-reconstruction using neural architecture search ensembles
- Authors: Romit Maulik, Romain Egele, Krishnan Raghavan, Prasanna Balaprakash
- Abstract summary: We present an automated approach to deep neural network (DNN) discovery and demonstrate how this may also be utilized for ensemble-based uncertainty quantification.
We highlight how the proposed method not only discovers high-performing neural network ensembles for our tasks, but also quantifies uncertainty seamlessly.
We demonstrate the feasibility of this framework for two tasks - forecasting from historical data and flow reconstruction from sparse sensors for the sea-surface temperature.
- Score: 0.8258451067861933
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Classical problems in computational physics such as data-driven forecasting
and signal reconstruction from sparse sensors have recently seen an explosion
in deep neural network (DNN) based algorithmic approaches. However, most DNN
models do not provide uncertainty estimates, which are crucial for establishing
the trustworthiness of these techniques in downstream decision making tasks and
scenarios. In recent years, ensemble-based methods have achieved significant
success for the uncertainty quantification in DNNs on a number of benchmark
problems. However, their performance on real-world applications remains
under-explored. In this work, we present an automated approach to DNN discovery
and demonstrate how this may also be utilized for ensemble-based uncertainty
quantification. Specifically, we propose the use of a scalable neural and
hyperparameter architecture search for discovering an ensemble of DNN models
for complex dynamical systems. We highlight how the proposed method not only
discovers high-performing neural network ensembles for our tasks, but also
quantifies uncertainty seamlessly. This is achieved by using genetic algorithms
and Bayesian optimization for sampling the search space of neural network
architectures and hyperparameters. Subsequently, a model selection approach is
used to identify candidate models for an ensemble set construction. Afterwards,
a variance decomposition approach is used to estimate the uncertainty of the
predictions from the ensemble. We demonstrate the feasibility of this framework
for two tasks - forecasting from historical data and flow reconstruction from
sparse sensors for the sea-surface temperature. We demonstrate superior
performance from the ensemble in contrast with individual high-performing
models and other benchmarks.
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