Article
Engineering, Civil
Vinh Ngoc Tran, Jongho Kim
Summary: A novel approach to surrogate data assimilation based on polynomial chaos expansion theory is presented, with eight types of surrogate filters proposed and validated. An advanced optimization scheme, the sequential experimental design-polynomial degree (SED-PD), is advised to address the shortcomings of existing sequential experimental design. It improves computational efficiency, reduces dimensions, and enhances the accuracy of real-time forecasting.
JOURNAL OF HYDROLOGY
(2021)
Article
Mathematics, Applied
Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott
Summary: This study discusses the forecasting of chaotic dynamical systems using noisy partial measurements data, with a focus on combining machine learning with knowledge-based models to improve predictions. By assimilating synthetic data and training machine learning models with partial measurements, it shows potential to correct imperfections in knowledge-based models and improve forecasting accuracy.
Article
Computer Science, Interdisciplinary Applications
Yuepeng Wang, Xuemei Ding, Kun Hu, Fangxin Fang, I. M. Navon, Guang Lin
Summary: In this study, a method combining DEIM and PC-EnKF is proposed to retrieve initial conditions in a high-dimensional space, achieving satisfactory reconstruction of the initial field with reduced computational cost. The experimental results demonstrate the effectiveness of the proposed algorithm.
JOURNAL OF COMPUTATIONAL PHYSICS
(2021)
Article
Geosciences, Multidisciplinary
Mao Ouyang, Keita Tokuda, Shunji Kotsuki
Summary: Controlling weather is a challenging task due to the chaotic nature of the atmosphere. This study uses a control simulation experiment on the Lorenz-63 model to demonstrate that variables can be controlled by adding perturbations with a constant magnitude. By investigating the impact of controls on system instability, the researchers propose an adaptive method to update the magnitude of perturbations, leading to a reduction in control times and magnitudes. The results suggest that understanding the effects of control on instability is beneficial for designing feasible methods to control the complex atmosphere.
NONLINEAR PROCESSES IN GEOPHYSICS
(2023)
Article
Mechanics
Zhiwen Deng, Chuangxin He, Yingzheng Liu
Summary: This paper focuses on the optimal sensor placement strategy based on a deep neural network for turbulent flow recovery within the data assimilation framework of the ensemble Kalman filter. The results demonstrate the effectiveness and robustness of the proposed strategy, showing that RANS models with EnKF augmentation were substantially improved over their original counterparts. The study concludes that the DNN-based OSP with the selection of the five most sensitive sensors can efficiently reduce the number of sensors while achieving similar or better assimilated performance.
Article
Multidisciplinary Sciences
Kevin Raeder, Timothy J. Hoar, Mohamad El Gharamti, Benjamin K. Johnson, Nancy Collins, Jeffrey L. Anderson, Jeff Steward, Mick Coady
Summary: An ensemble Kalman filter reanalysis data set with a global, 80 member ensemble spanning from 2011 to 2019 is archived, providing opportunities for robust statistical analysis and machine learning training.
SCIENTIFIC REPORTS
(2021)
Article
Engineering, Marine
Shaokun Deng, Zheqi Shen, Shengli Chen, Renxi Wang
Summary: The initial ensemble has an impact on the performance of ensemble-based assimilation techniques. The differences in the initial ensemble affect the convergence rate of assimilation, but all experiments eventually reach convergence. Sea surface height and sea surface salinity are more sensitive to the initial ensemble. The white-noise perturbation scheme has the largest effect, and the influence of different initial ensembles on sea surface height is concentrated in the region of the Antarctic Circumpolar Current.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Energy & Fuels
Mehdi Safari, Mohammad Javad Ameri, Raoof Gholami, Ali Rahimi
Summary: Undesirable water production due to water coning in hydrocarbon reservoirs has been a significant issue, affecting oil production and well shutdown. A new approach combining boundary control and Ensemble Kalman Filter showed promising results in controlling water coning and estimating reservoir permeability accurately.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Chemistry, Multidisciplinary
Yifan Chen, Feifeng Cao, Xiangyong Meng, Weiping Cheng
Summary: This study introduces a new data assimilation model based on the ensemble Kalman filter (EnKF) that accurately predicts water levels in river networks. Compared to existing forecasting models that are mainly used for single-channel rivers, the EnKF-based model performs better in simulating complex river networks.
APPLIED SCIENCES-BASEL
(2023)
Article
Environmental Sciences
Jingyao Luo, Hong Li, Ming Xue, Yijie Zhu
Summary: This study investigates the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model. The results show that the assimilation of reflectivity data can significantly improve the forecast skills for intensity, precipitation, and track of a landfall typhoon. Simultaneously updating a full set of state variables can help obtain more balanced analytical fields.
Article
Meteorology & Atmospheric Sciences
Eviatar Bach, Michael Ghil
Summary: Data assimilation aims to optimally combine partial and noisy model forecasts and observations. Multi-model data assimilation generalizes the variational or Bayesian formulation of the Kalman filter and is proven to be the minimum variance linear unbiased estimator. In this study, a multi-model ensemble Kalman filter (MM-EnKF) based on this framework is formulated and implemented. The MM-EnKF can combine multiple model ensembles for both data assimilation and forecasting in a flow-dependent manner by providing adaptive model error estimation and matrix-valued weights for the separate models and observations. Numerical experiments using the Lorenz96 model show that the MM-EnKF results in significant error reductions compared to the best model and an unweighted multi-model ensemble in terms of probabilistic and deterministic metrics.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(2023)
Article
Engineering, Ocean
Shintaro Gomi, Tsutomu Takagi, Katsuya Suzuki, Rika Shiraki, Ichiya Ogino, Shigeru Asaumi
Summary: A control method for changing the geometry of a fishing net was proposed, utilizing data assimilation to estimate unknown parameters and achieve the intended net geometry. The automatic control system was validated through numerical simulation experiments, demonstrating the successful control of net geometry using the extended Kalman filter.
APPLIED OCEAN RESEARCH
(2021)
Article
Computer Science, Interdisciplinary Applications
Benjamin Christoffersen
Summary: The dynamichazard package implements state space models for computationally efficient modeling of time-varying parameters in survival analysis. The author covers the models and estimation methods implemented in dynamichazard, applies them to a large dataset, and performs a simulation study to illustrate computation time and performance. One method is compared with other models in R allowing for various features like left-truncation and time-varying covariates.
JOURNAL OF STATISTICAL SOFTWARE
(2021)
Article
Environmental Sciences
Jun Tang, Shimeng Zhang, Xingliang Huo, Xuequn Wu
Summary: In this study, a data assimilation model based on the local ensemble Kalman filter (LEnKF) and the International Reference Ionosphere 2016 (IRI-2016) model is proposed to assimilate ionospheric total electron content (TEC) observations from GNSS. The results demonstrate that the assimilation method is able to improve the accuracy of GNSS positioning and show good agreement with other estimation products.
Article
Engineering, Petroleum
Zhen Zhang, Xupeng He, Marwah AlSinan, Hyung Kwak, Hussein Hoteit
Summary: This study proposes a new robust method using Bayesian Markov chain Monte Carlo (MCMC) to perform assisted history matching under uncertainties. The proposed method includes multiresolution low-fidelity models, LSTM network combined with Bayesian optimization, and Bayesian MCMC to obtain accurate predictions with narrow ranges of uncertainties.
Article
Engineering, Multidisciplinary
Roger Ghanem, Christian Soize, Loujaine Mehrez, Venkat Aitharaju
Summary: This article presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. The method utilizes regression and joint density function to systematically explore the interdependence between fine scale and coarse observables, which can be used for prognosis and design of complex material systems.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2022)
Article
Engineering, Civil
Ruda Zhang, Roger Ghanem
Summary: Understanding driver behavior in on-demand mobility services is crucial for designing efficient and sustainable transport models. We provide a game-theoretic model of driver search strategy and learning dynamics, interpret the collective outcome in a thermodynamic framework, and verify its various implications empirically. By studying 870 million trips in New York City, we show that the equilibrium well explains the spatiotemporal patterns of driver search behavior.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Christian Soize, Roger Ghanem
Summary: PLoM has addressed difficult supervised problems for small data sets, but limitations have been observed when data volume is very small and dimensions are close. Therefore, a novel extension based on partitioning independent random vectors has been introduced to improve PLoM's algorithm and propose a mathematical result for quantifying the concentration of probability measures.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2022)
Article
Construction & Building Technology
Angela J. Haddad, George A. Saad, Ghassan R. Chehab
Summary: This study develops a probabilistic framework using Ensemble Kalman Filter (EnKF) techniques to update parameters in generic rutting predictive models while considering uncertainties. By continuously updating the models and minimizing prediction errors, the framework improves the accuracy of rutting predictions.
INTERNATIONAL JOURNAL OF PAVEMENT ENGINEERING
(2023)
Article
Engineering, Multidisciplinary
Zhiheng Wang, Roger Ghanem
Summary: This article explores the importance of sensitivity analysis and reliability assessment in structural and system safety. It introduces a modified extended polynomial chaos expansion approach to evaluate the impact of distribution parameters on the output. The methodology provides more informative and efficient reliability sensitivity indices.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Multidisciplinary
Xiaoshu Zeng, Roger Ghanem
Summary: This work addresses the accurate stochastic approximations in high-dimensional parametric space using uncertainty quantification tools. A novel approach combining basis adaptation and projection pursuit regression is proposed to simultaneously learn the optimal low-dimensional spaces and polynomial chaos expansions from given data. The method demonstrates the ability to discover low-dimensional manifolds and learn surrogate models with high accuracy with limited data.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Geological
Imad El Chiti, Shadi Najjar, George Saad, Salah Sadek
Summary: This paper studies the development of lateral stresses behind rigid walls in the context of p-y curves, and provides insight into the p-y response behind rigid walls under static and cyclic loading conditions. The results indicate that the proposed experimental setup yields repeatable and consistent measurements of the p-y response, and the p-y curves exhibit highly nonlinear behavior.
GEOTECHNICAL TESTING JOURNAL
(2022)
Article
Nuclear Science & Technology
Mehrdad Aghagholizadeh, Bora Gencturk, Roger Ghanem, Anna Arcaro
Summary: This paper investigates the use of frequency response functions (FRFs) to detect abnormalities in the contents of a sealed spent-nuclear fuel canister when subjected to external vibration. Both finite element simulations and experimental analyses were conducted to investigate a mock-up fully loaded canister basket system. The study demonstrates that dynamic vibration measurements on the outside of a canister can provide valuable information for detecting physical abnormalities.
ANNALS OF NUCLEAR ENERGY
(2023)
Article
Engineering, Industrial
Zhiheng Wang, Philippe Hawi, Sami Masri, Venkat Aitharaju, Roger Ghanem
Summary: This paper proposes a computational framework for stochastic multiscale analysis of material systems, considering both parametric uncertainties and modeling errors. The framework utilizes a generalized extended polynomial chaos expansion (gEPCE) to propagate uncertainties and provide probabilistic representations of specific quantities of interest (QoI). Sensitivity measures are constructed using kernel density estimation (KDE) and directional derivatives to assess the impacts of individual and combined uncertainties.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Geological
Imad El Chiti, Shadi Najjar, George Saad, Salah Sadek, El Chiti, S. Sadek
Summary: This paper studies the development of lateral earth pressure behind rigid walls using p-y curves. A laboratory-scale retaining wall prototype was used to provide insight on the p-y response under static and cyclic loading conditions. The results show that the p-y relationship is highly nonlinear and not adequately represented by the simple elastic-perfectly plastic model. The cyclic tests indicate a process of densification that dominates the sand's volumetric tendency, leading to an increase in stiffness and maximum pressure at the passive side after 10 loading cycles.
GEOTECHNICAL TESTING JOURNAL
(2023)
Article
Engineering, Civil
Imad Elchiti, George Saad, Shadi S. Najjar
Summary: For structures with underground basement walls, the interaction between the side soil and the walls affects the system response. This study focuses on developing a non-linear p-y model to accurately represent the relationship between lateral earth pressure and wall displacement. The results show that the stress-displacement response is highly non-linear and influenced by factors such as wall height, relative density, and depth below the ground surface. The proposed truncated hyperbolic model provides an improved representation of the passive p-y curves.
GEOMECHANICS AND ENGINEERING
(2023)
Correction
Computer Science, Artificial Intelligence
Cosmin Safta, Roger G. Ghanem, Michael J. Grant, Michael Sparapany, Habib N. Najm
DATA-CENTRIC ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Cosmin Safta, Roger G. Ghanem, Michael J. Grant, Michael Sparapany, Habib N. Najm
Summary: This article demonstrates the utilization of unsupervised probabilistic learning techniques in the analysis of planetary reentry trajectories. Optimal trajectories were generated using a three-degree-of-freedom model for the training dataset. By employing a diffusion map approach, the algorithm extracts the intrinsic structure within the data and augments the original dataset with statistically consistent samples. These samples are then utilized in a path planning algorithm to adapt to real-time changing mission objectives.
DATA-CENTRIC ENGINEERING
(2022)
Article
Mathematics, Applied
Ruda Zhang, Roger Ghanem
Summary: Probabilistic models of data sets often exhibit geometric structures, which can be exploited in probabilistic learning. The normal-bundle bootstrap (NBB) method presented here generates new data that preserve the geometric structure of a given data set. Inspired by algorithms for manifold learning and concepts in differential geometry, the method estimates the data manifold as a density ridge and constructs new data by bootstrapping projection vectors to reduce overfitting.
SIAM JOURNAL ON MATHEMATICS OF DATA SCIENCE
(2021)
Article
Mathematics, Interdisciplinary Applications
O. Ezvan, X. Zeng, R. Ghanem, B. Gencturk
Summary: This paper investigates the dynamic characteristics of a multilevel structure for the transportation and storage of spent nuclear fuel from commercial power plants, focusing on developing a computational model to accurately represent the structural dynamics of the canister. The use of Craig-Bampton substructuring for modal analysis, along with the removal of least dominant substructural modes, results in an efficient methodology for reducing the CB model and improving computational efficiency.
COMPUTATIONAL MECHANICS
(2021)