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1. Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application

   https://doi.org/10.1038/s41598-022-11049-3  

   Licata, Richard J. ; Mehta, Piyush M. ( May 2022 , Scientific Reports)

   Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth’s upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and computation of probability of collision between two space objects in low Earth orbit (LEO) for decision making in space operations. Even with (assumed) perfect forecast of model drivers, our incomplete knowledge of the system results in often inaccurate thermospheric neutral mass density predictions. Continuing efforts are being made to improve model accuracy, but density models rarely provide estimates of confidence in predictions. In this work, we propose two techniques to develop nonlinear ML regression models to predict thermospheric density while providing robust and reliable uncertainty estimates: Monte Carlo (MC) dropout and direct prediction of the probability distribution, both using the negative logarithm of predictive density (NLPD) loss function. We show the performance capabilities for models trained on both local and global datasets. We show that the NLPD loss provides similar results for both techniques but the direct probability distribution prediction method has a much lower computational cost. For the global model regressed on the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, we achieve errors of approximately 11% on independent test data with well-calibrated uncertainty estimates. Using an in-situ CHAllenging Minisatellite Payload (CHAMP) density dataset, models developed using both techniques provide test error on the order of 13%. The CHAMP models—on validation and test data—are within 2% of perfect calibration for the twenty prediction intervals tested. We show that this model can also be used to obtain global density predictions with uncertainties at a given epoch.

    

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2. Large-Eddy Simulations of the Tropical Cyclone Boundary Layer at Landfall in an Idealized Urban Environment

   https://doi.org/10.1175/JAMC-D-23-0024.1  

   Rozoff, Christopher M. ; Nolan, David S. ; Bryan, George H. ; Hendricks, Eric A. ; Knievel, Jason C. ( November 2023 , Journal of Applied Meteorology and Climatology)

   Populated urban areas along many coastal regions are vulnerable to landfalling tropical cyclones (TCs). To the detriment of surface parameterizations in mesoscale models, the complexities of turbulence at high TC wind speeds in urban canopies are presently poorly understood. Thus, this study explores the impacts of urban morphology on TC-strength winds and boundary layer turbulence in landfalling TCs. To better quantify how urban structures interact with TC winds, large-eddy simulations (LESs) are conducted with the Cloud Model 1 (CM1). This implementation of CM1 includes immersed boundary conditions (IBCs) to represent buildings and eddy recycling to maintain realistic turbulent flow perturbations. Within the IBCs, an idealized coastal city with varying scales is introduced. TC winds impinge perpendicularly to the urbanized coastline. Numerical experiments show that buildings generate distinct, intricate flow patterns that vary significantly as the city structure is varied. Urban IBCs produce much stronger turbulent kinetic energy than is produced by conventional surface parameterizations. Strong effective eddy viscosity due to resolved eddy mixing is displayed in the wake of buildings within the urban canopy, while deep and enhanced effective eddy viscosity is present downstream. Such effects are not seen in a comparison LES using a simple surface parameterization with high roughness values. Wind tunneling effects in streamwise canyons enhance pedestrian-level winds well beyond what is possible without buildings. In the arena of regional mesoscale modeling, this type of LES framework with IBCs can be used to improve parameters in surface and boundary layer schemes to more accurately represent the drag coefficient and the eddy viscosity in landfalling TC boundary layers.

   This is among the first large-eddy simulation model studies to examine the impacts of tropical cyclone–like winds around explicitly resolved buildings. This work is a step forward in bridging the gap between engineering studies that use computational fluid dynamics models or laboratory experiments for flow through cities and mesoscale model simulations of landfalling tropical cyclones that use surface parameterizations specialized for urban land use.

    

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3. Training Machine Learning Surrogate Models From a High‐Fidelity Physics‐Based Model: Application for Real‐Time Street‐Scale Flood Prediction in an Urban Coastal Community

   https://doi.org/10.1029/2019WR027038  

   Zahura, Faria T. ; Goodall, Jonathan L. ; Sadler, Jeffrey M. ; Shen, Yawen ; Morsy, Mohamed M. ; Behl, Madhur ( October 2020 , Water Resources Research)

   Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real‐time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Forest (RF), to serve as a surrogate model for urban flood predictions. The surrogate model was trained to relate topographic and environmental features to hourly water depths simulated by a high‐resolution 1‐D/2‐D physics‐based model at 16,914 road segments in the coastal city of Norfolk, Virginia, USA. Two training scenarios for the RF model were explored: (i) training on only the most flood‐prone street segments in the study area and (ii) training on all 16,914 street segments in the study area. The RF model yielded high predictive skill, especially for the scenario when the model was trained on only the most flood‐prone streets. The results also showed that the surrogate model reduced the computational run time of the physics‐based model by a factor of 3,000, making real‐time decision support more feasible compared to using the full physics‐based model. We concluded that machine learning surrogate models strategically trained on high‐resolution and high‐fidelity physics‐based models have the potential to significantly advance the ability to support decision making in real‐time flood management within urban communities.

    

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4. Design representation for performance evaluation of 3D shapes in structure-aware generative design

   https://doi.org/10.1017/dsj.2023.25  

   Li, Xingang ; Xie, Charles ; Sha, Zhenghui ( January 2023 , Design Science)

   Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.

    

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5. SATELLITE DRAG COEFFICIENT MODELING AND ORBIT UNCERTAINTY QUANTIFICATION USING STOCHASTIC MACHINE LEARNING TECHNIQUES

   Paul, S.N. ; Sheridan, L. ; Mehta, P. ; Huzurbazar, S. ( January 2021 , American Astronomical Society meeting)

   The rapidly increasing congestion in the low Earth environment makes the modeling of uncertainty in atmospheric drag force a critical task, affecting space situational awareness (SSA) activities like the probability of collision estimation. A key element in atmospheric drag modeling is the assessment of uncertainty in the atmospheric drag coefficient estimate. While atmospheric drag coefficients for space objects with known characteristics can be computed numerically, they suffer from large computational costs for practical applications. In this work, we use cost-effective data-driven stochastic methods for modeling the drag coefficients of objects in the low Earth orbit (LEO) region. The training data is generated using the numerical Test Particle Monte Carlo (TPMC) method. TPMC is simulated with Cercignani–Lampis–Lord (CLL) gas-surface interaction (GSI) model. Mehta et al. [1] use a Gaussian process regression (GPR) model to predict satellite drag coefficient, but the authors did not estimate the predictive uncertainty. The first part of this research extends the work by Mehta et al. [1] by fitting a GPR model to the training data and performing predictive uncertainty estimation. The results of the Gaussian fit are then compared against a deep neural network (DNN) model aided by the Monte Carlo dropout approach. To the best of our knowledge, this is the first study to use the aforementioned stochastic deep learning algorithm to perform predictive uncertainty estimation of the estimated satellite drag coefficient. Apart from the accuracy of the models, we also undertake the task of calibrating the models. Simulations are carried out for a spherical satellite followed by the Champ satellite. Finally, quantification of the effect of drag coefficient uncertainty on orbit prediction is carried out for different solar activity and geomagnetic activity levels. 

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