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- Creator:
- Chen, Hongfan, Chen, Yang, Huang, Zhenguang, Zou, Shasha, Huan, Xun, and Toth, Gabor
- Description:
- Accurately predicting the horizontal component of the ground magnetic field perturbation (dBH), which can be used to calculate the Geomagnetically Induced Currents (GICs), is crucial for estimating the space weather impact of geomagnetic disturbances. In this work, we develop a new data-driven model GeoDGP using deep Gaussian process (DGP), which is a Bayesian non-parametric approach. The model provides global probabilistic forecasts of dBH at 1-minute time cadence and with arbitrary spatial resolutions. We evaluate the model comprehensively on a wide range of geomagnetic storms, including the 2024 Gannon extreme storm. The results show that GeoDGP significantly outperforms the state-of-the-art physics-based first-principles Space Weather Modeling Framework (SWMF) Michigan Geospace model and the data-driven DAGGER model.
- Keyword:
- Space Weather, Uncertainty Quantification, Machine Learning, and Bayesian Inference
- Citation to related publication:
- Chen, H., et al. (2024). GeoDGP: One-Hour Ahead Global Probabilistic Geomagnetic Perturbation Forecasting using Deep Gaussian Process.
- Discipline:
- Science and Engineering
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- Creator:
- Chen, Hongfan, Sachdeva, Nishtha, Huang, Zhenguang, van der Holst, Bart, Manchester, Ward, Jivani, Aniket, Zou, Shasha, Chen, Yang, Huan, Xun, and Toth, Gabor
- Description:
- In this study, we show that coronal mass ejection (CME) simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white-light (WL) coronagraph observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. L1 observations are used to constrain the background solar wind, while LASCO coronagraph observations filter the initial ensemble simulations by constraining the simulated CME propagation speed. We then construct probabilistic predictions for CME arrival time using the data-assimilated ensemble. Scripts in this work are written in R, Python and Julia.
- Keyword:
- Data Assimilation, Uncertainty Quantification, and Space Weather
- Citation to related publication:
- https://doi.org/10.1029/2024SW004165
- Discipline:
- Engineering
-
- Creator:
- Aksoy, Doruk and Kim, Donghak
- Description:
- This dataset contains snapshots from simulations of a hexagonal self oscillating gel sheet defined via a triangular lattice. The lattice has stretching springs between neighboring vertices and bending springs with energy proportional to the square of the angle between neighboring traingular faces. The motion of the lattice is driven by time- and space-varying distributions of the rest lengths of the stretching springs. In the motivating experiments on thin gel sheets, there are chemical waves, radial or spiral in form, that induce local swelling of the sheets. As a simple model, this dataset considers radial or planar (unidirectional) traveling waves in the simulations. The sheet is modeled as a flat hexagon of radius 1 with an equilateral triangular triangle lattice mesh, with initially uniform mesh spacing of 1/33, resulting in 3367 mesh points. A small out-of-plane perturbation is applied and the motion evolves over the sheet over time. The sheet is modeled to have damped dynamics. However for large enough wave amplitudes, the sheet rapidly buckles into shapes with time-varying distributions of curvature, large in magnitude. For more information on the simulation that generated the data, please refer to "Semi-implicit methods for the dynamics of elastic sheets,” at Journal of Computational Physics by Alben et al. For an example SciML application that considers this dataset, please refer to "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications by Aksoy et al.
- Keyword:
- Soft robotics, Partial Differential Equations, Scientific Simulations, and Chaotic Systems
- Citation to related publication:
- Alben, Silas, et al. "Semi-implicit methods for the dynamics of elastic sheets." Journal of Computational Physics 399 (2019): 108952., Aksoy, Doruk, et al. "Inverse design of self-oscillatory gels through deep learning." Neural Computing and Applications 34.9 (2022): 6879-6905., Aksoy, Doruk, et al. "An incremental tensor train decomposition algorithm." SIAM Journal on Scientific Computing 46.2 (2024): A1047-A1075., and Aksoy, Doruk, and Alex A. Gorodetsky. "Incremental Hierarchical Tucker Decomposition." arXiv preprint arXiv:2412.16544 (2024).
- Discipline:
- Engineering and Science
-
- Creator:
- Dong, Jiayuan, Liao, Jiankan, Huan, Xun, and Cooper, Daniel R.
- Description:
- We apply expert elicitation to assign informative prior to material flow analysis and conduct Bayesian inference for parameter and data noise learning.
- Keyword:
- Bayesian inference, Bayes factor, data noise, prior elicitation and aggregation, and uncertainty quantification
- Citation to related publication:
- Dong, Jiayuan, Jiankan Liao, Xun Huan, and Daniel Cooper. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference." Journal of Industrial Ecology 27, no. 4 (2023): 1105-1122.
- Discipline:
- Engineering
-
- Creator:
- Liao, Jiankan, Deng, Sidi, Huan, Xun, and Cooper, Daniel R.
- Description:
- We apply Bayesian inference to reduce network structure uncertainty on material flow analysis (MFA) and demonstrate the methodology through a case study on U.S. steel flow. In addition, we derive an input/output-based analysis to conduct decision-making based on the uncertainty results from MFA
- Keyword:
- Bayesian inference, Network structure uncertainty, Bayesian model selection, and Input/output analysis
- Citation to related publication:
- Liao, Jiankan, Deng, Sidi, Xun Huan, and Daniel Cooper. "Bayesian Model Selection for Network Discrimination and Risk-informed Decision Making in Material Flow Analysis." arXiv preprint arXiv:2501.05556 (2025).
- Discipline:
- Engineering
-
- Creator:
- Teague, Seth, Yu, Zhiyuan, and Heemskerk, Idse
- Description:
- Images were collected as part of a project investigating the interpretation of BMP signaling dynamics by differentiating human pluripotent stem cells. Image files are in the proprietary Imaris (.ims) file format. MATLAB and Python code for image processing and quantification is provided with the data and at https://github.com/seth414/HeemskerkLabMethods. Processed data originally published in Teague et al., 2024 (see below).
- Keyword:
- cell tracking, human pluripotent stem cells, immunofluorescence, and signaling dynamics
- Citation to related publication:
- Teague, S., Primavera, G., Chen, B. et al. Time-integrated BMP signaling determines fate in a stem cell model for early human development. Nat Commun 15, 1471 (2024). https://doi.org/10.1038/s41467-024-45719-9
- Discipline:
- Health Sciences and Engineering
-
- Creator:
- Wu, Wenbing, Kadar, Alain, Lee, Sang Hyun, Jung, Hong Ju, Park, Bum Chul, Raymond, Jeffery, Tsotsis, Thomas, Cesnik, Carlos, Glotzer, Sharon, Goss, Valerie, and Kotov, Nicholas
- Description:
- The goal of this project is to relate properties of nanowire networks to their structure. The structure of these networks was determined from electron and atomic force microscopy, which were used as the basis for property predictions. Properties include sheet resistance, conductive anisotropy, absorption spectra, and current capacity.
- Keyword:
- structural complexity, nanowires, graph theory (GT), complex particle systems, complex composites, correlated disorder
- Citation to related publication:
- Preprint: https://arxiv.org/pdf/2310.15369
- Discipline:
- Engineering
-
- Creator:
- Figueroa, Carlos A., Computational Vascular Biomechanics Lab, University of Michigan, and et al.
- Description:
- This collection concerns the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. , The minimum specifications to run CRIMSON are: Any AMD64 CPU (note: Intel Core i series are AMD64), Windows (only tested on Windows 10 but might work on Windows 7), 8 GB of RAM , If you are running non-trivial models you will want to have: Quad core CPU or higher, Solid state drive for storing data, Windows, 16 GB of RAM, Dedicated discrete GPU for rendering models. , and Software in this collection is a snapshot; please visit https://github.com/carthurs/CRIMSONGUI & www.crimson.software for more general information and the most up to date version of the software.
- Keyword:
- Blood Flow Simulation, Patient-specific, Open-source Software, Image-based simulation, Cardiovascular Medical Image, Segmentation, and Finite Element Simulation
- Citation to related publication:
- CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation C.J. Arthurs, R. Khlebnikov, A. Melville, et al. bioRxiv 2020.10.14.339960; doi: https://doi.org/10.1101/2020.10.14.339960
- Discipline:
- Health Sciences and Engineering
4Works -
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
-
- Creator:
- Lee, Shih Kuang, Tsai, Sun Ting, and Glotzer, Sharon C.
- Description:
- The trajectory data and codes were generated for our work "Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation" (amidst peer review process). The data sets contain trajectory data in GSD file format for 7 test systems, including cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with two aspect ratios, and truncated shapes with two degrees of truncation. Besides, the corresponding Python code and Jupyter notebook used to perform data augmentation, MLP classifier training, and MLP classifier testing are included.
- Keyword:
- Machine Learning, Colloids Self-Assembly, Crystallization, and Order Parameter
- Citation to related publication:
- https://doi.org/10.48550/arXiv.2312.11822
- Discipline:
- Other, Science, and Engineering
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