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Process-based numerical simulation, includ- ing for climate modeling applications, is compute- and resource-intensive, requiring extensive customization and hand-engineering for encoding governing equations and other domain knowledge. On the other hand, modern deep learning employs a much simplified and efficient computational workflow, and has been showing impres- sive results across myriad applications in computational sciences. In this work, we investigate the potential of deep generative learning models, specifically conditional Gen- erative Adversarial Networks (cGANs), to simulate the output of a physics-based model of the spatial distribution of the water content of mountain snowpack, or snow water equivalent (SWE). We show preliminary results indicating that the cGANs model is able to learn map- pings between meteorological forcing (e.g., minimum and maximum temperature, wind speed, net radiation, and precipitation) and SWE output. Moreover, informing the model with simple domain-inspired physical constraints results in higher model accuracy, and lower training time. Thus Physics-Informed cGANs provide a means for fast and accurate SWE modeling that can have significant impact in a variety of applications (e.g., hydropower forecasting, agriculture, and water supply management).
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Combining Empirical and Physics-Based Models for Solar Wind Prediction
Solar wind modeling is classified into two main types: empirical models and physics-based models, each designed to forecast solar wind properties in various regions of the heliosphere. Empirical models, which are cost-effective, have demonstrated significant accuracy in predicting solar wind at the L1 Lagrange point. On the other hand, physics-based models rely on magnetohydrodynamics (MHD) principles and demand more computational resources. In this research paper, we build upon our recent novel approach that merges empirical and physics-based models. Our recent proposal involves the creation of a new physics-informed neural network that leverages time series data from solar wind predictors to enhance solar wind prediction. This innovative method aims to combine the strengths of both modeling approaches to achieve more accurate and efficient solar wind predictions. In this work, we show the variability of the proposed physics-informed loss across multiple deep learning models. We also study the effect of training the models on different solar cycles on the model’s performance. This work represents the first effort to predict solar wind by integrating deep learning approaches with physics constraints and analyzing the results across three solar cycles. Our findings demonstrate the superiority of our physics-constrained model over other unconstrained deep learning predictive models.
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- PAR ID:
- 10516992
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Universe
- Volume:
- 10
- Issue:
- 5
- ISSN:
- 2218-1997
- Page Range / eLocation ID:
- 191
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/universe10050191
