arXiv subjects

Guangyao Wang

Publications and source records attributed to Guangyao Wang.

10 recordsLinked to original sources

arXiv2026

A Hybrid POD-Autoencoder Framework for Reduced Order Modeling of Turbulent Flow via Strategic Field Decomposition

This study proposes a hybrid reduced-order modeling (ROM) framework for the simulation of turbulent flow. The central idea is to decompose flow dynamics according to their temporal characteristics and predict the resulting components individually. The full field is first divided into a sub-field represented by a limited number of proper orthogonal decomposition (POD) modes (named as POD-retained field) and the corresponding residual sub-field (named as POD-truncated field). A frequency-informed POD strategy identifies the retained modes by considering both modal energy and dominant frequency. The evolution of retained POD coefficients, which feature similar temporal scales, is described using a vector autoregressive (VAR) model. In parallel, the POD-truncated field is compressed into a low-dimensional latent space using a Fourier-neural-operator-based Koopman $\beta$-variational autoencoder (FK-$\beta$-VAE), with the latent variables subsequently predicted by a switching-VAR model. Turbulent statistics of the full field are recovered by combining the contributions from the two components. The framework is assessed using turbulent channel flow at a friction Reynolds number of $110$. The predicted Reynolds-stress components, turbulent kinetic energy (TKE), and dominant wavenumber spectra show good agreement with the reference. Moreover, in comparison with an alternative framework of full-field modeling (i.e., without field decomposition), the proposed framework yields more accurate and robust long-term statistical predictions.

physics.flu-dyn↗

arXiv2026

Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction

Data assimilation (DA) integrates numerical model forecasts with observations to achieve the optimal state estimation. Ensemble-based methods, such as the ensemble Kalman filter (EnKF), are widely used for state estimation for high-dimensional and nonlinear dynamic systems. However, their performance strongly depends on the ensemble size, therefore causing a tradeoff problem between analysis accuracy and computational cost. To address this problem, this study presents a machine learning-based EnKF framework that maintains high accuracy with a relatively small ensemble size. Specifically, a multilayer perceptron (MLP) function is built to predict the difference between the forecast error covariances estimated from a limited ensemble and a sufficiently large ensemble, with the latter being assumed to be an accurate approximation of the underlying truth. This predicted covariance difference term is then incorporated into the EnKF algorithm via an element-wise scaling strategy, resulting in an amended forecast covariance matrix that better approximates the true uncertainty level and sequentially produces more accurate analysis results. To demonstrate the feasibility and robustness of the proposed algorithm, we perform a set of numerical experiments with the Lorenz-63 and Lorenz-96 systems under various configurations, and the results consistently indicate that the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient. This approach provides a practical and feasible pathway to accurate and computationally efficient data assimilation for high-dimensional and nonlinear dynamic systems.

physics.ao-ph↗

arXiv2025

Impact of Wave Interference on the Consistency Relations of Internal Gravity Waves near the Ocean Bottom

Consistency relations of internal gravity waves (IGWs) describe ratios of cross-spectral quantities as functions of frequency. It has been a common practice to evaluate the measured or simulated signals (e.g., time series of velocity, density, etc.) against the consistency relations, as a way to determine whether an oceanic field of interest is comprised of IGWs. One such study is carried out in Nelson et al. (JGR Oceans, 125(5), 2020, e2019JC015974), which certifies that the ocean interior field in a numerical simulation of a region southwest of Hawaii is dominated by IGWs, through evaluating the consistency relations derived from time series at a depth of 620 m. However, we find that when the same procedure is applied at greater depths (e.g., 2362 m, 3062 m, and 4987 m), a clear deviation of the simulated signal from the classical consistency relations is observed. In this paper, we identify the reason for the unexpected deviation and show that it is a general phenomenon due to interference of low vertical modes under the reflection by the ocean bottom. We further derive a new set of formulae to characterize the consistency relations of these low modes and validate these formulae using model output.

physics.ao-ph↗

arXiv2025

Small Ensemble-based Data Assimilation: A Machine Learning-Enhanced Data Assimilation Method with Limited Ensemble Size

Ensemble-based data assimilation (DA) methods have become increasingly popular due to their inherent ability to address nonlinear dynamic problems. However, these methods often face a trade-off between analysis accuracy and computational efficiency, as larger ensemble sizes required for higher accuracy also lead to greater computational cost. In this study, we propose a novel machine learning-based data assimilation approach that combines the traditional ensemble Kalman filter (EnKF) with a fully connected neural network (FCNN). Specifically, our method uses a relatively small ensemble size to generate preliminary yet suboptimal analysis states via EnKF. A FCNN is then employed to learn and predict correction terms for these states, thereby mitigating the performance degradation induced by the limited ensemble size. We evaluate the performance of our proposed EnKF-FCNN method through numerical experiments involving Lorenz systems and nonlinear ocean wave field simulations. The results consistently demonstrate that the new method achieves higher accuracy than traditional EnKF with the same ensemble size, while incurring negligible additional computational cost. Moreover, the EnKF-FCNN method is adaptable to diverse applications through coupling with different models and the use of alternative ensemble-based DA methods.

cs.LG↗

arXiv2025

Ensemble-Based Data Assimilation for Material Model Characterization in High-Velocity Impact

High-fidelity simulations are essential for predicting material behavior under high-velocity impact (HVI), but their accuracy depends on material models and parameters that are often calibrated by manual fitting to multiple costly experiments. In this study, we develop an ensemble-based data assimilation framework for automatic calibration of selected plasticity, fracture, and equation-of-state (EOS) parameters in HVI simulations using data from a single HVI test. The framework combines Smoothed Particle Hydrodynamics, the ensemble Kalman filter (EnKF), and adaptive covariance inflation to mitigate variance collapse. A simple benchmark first shows that the framework is at least one order of magnitude more computationally efficient than Markov chain Monte Carlo at comparable identification accuracy. We then use synthetic back-face deflection data for an AZ31B magnesium plate to identify representative parameters in the Johnson-Cook plasticity and fracture models and the Mie-Gruneisen EOS. Results under under-biased, over-biased, and limited-observation cases show that parameters to which the data are sufficiently sensitive can be accurately recovered within about five iterations, with convergent ensemble standard deviations. In contrast, insensitive parameters tend to converge to incorrect values and retain large ensemble spreads. With fewer observations, convergence is still achieved for sensitive parameters but requires more iterations. Under extreme prior bias, a parameter rejuvenation strategy drives sensitive parameters toward the true values even when the truth lies outside the initial ensemble spread. These results show that ensemble standard deviation provides a practical diagnostic for parameter sensitivity, identifiability, and potential non-uniqueness. Overall, the proposed framework offers an efficient and robust approach for material model characterization in HVI problems.

cond-mat.mtrl-sci↗

arXiv2025

Data Assimilation-based Simultaneous Phase-Resolved Ocean Wave and Ship Motion Forecast

This paper presents a data-assimilation (DA)-based approach to forecast the phase-resolved wave evolution process and ship motion, which is developed by coupling the high-order spectral method (HOS), ensemble Kalman filter (EnKF), and a Cummins-equation-based ship model (CMI). With the developed EnKF-HOS-CMI method, the observation data for wave, ship, or both can be incorporated into the model, therefore producing the optimal analysis results. The developed method is validated and tested based on a synthetic problem on the motions of an irregular wave field and a box-shaped free-floating ship. We show that the EnKF-HOS-CMI method achieves much higher accuracy in the long-term simulation of nonlinear phase-resolved wave field and ship motion in comparison with the HOS-CMI method. Also, the ship parameters are estimated accurately by using a parameter-augmented state space in EnKF.

eess.SY↗

arXiv2024

Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types

Recent advancements in large language models (LLMs) have significantly advanced text-to-SQL systems. However, most LLM-based methods often narrowly focus on SQL generation, neglecting the complexities of real-world conversational queries. This oversight can lead to unreliable responses, particularly for ambiguous questions that cannot be directly addressed with SQL. To bridge this gap, we propose MMSQL, a comprehensive test suite designed to evaluate the question classification and SQL generation capabilities of LLMs by simulating real-world scenarios with diverse question types and multi-turn Q&A interactions. Using MMSQL, we assessed the performance of popular LLMs, including both open-source and closed-source models, and identified key factors impacting their performance in such scenarios. Moreover, we introduce an LLM-based multi-agent framework that employs specialized agents to identify question types and determine appropriate answering strategies. Our experiments demonstrate that this approach significantly enhances the model's ability to navigate the complexities of conversational dynamics, effectively handling the diverse and complex nature of user queries. Our dataset and code are publicly available at https://mcxiaoxiao.github.io/MMSQL.

cs.CL↗

arXiv2024

Distribution of plastics of various sizes and densities in the global ocean from a 3D Eulerian model

We develop a 3D Eulerian model to study the transport and distribution of microplastics in the global ocean. Among other benefits that will be discussed in the paper, one unique feature of our model is that it takes into consideration the effect of properties of particles (size and density, the former for the first time) to their vertical terminal velocity. With ocean current velocity taken from ECCOv4r4, a dataset generated from a data-assimilated MITgcm reanalysis, our model is integrated for 26 years for particles of different properties with their stationary patterns studied. We find that only low-density particles with sufficient size (e.g. density $900kg/m^3$ with size $\gtrsim 10 \mu m$) aggregate in the five subtropical gyres observed in previous studies. In contrast, particles of smaller size ($\sim 1 \mu m$), irrespective of their density, behave like neutrally buoyant particles with a weaker pattern on the surface and a deeper penetration into depth (up to about 1km deep). In addition, we observe seasonal variations of floating particle concentration on the ocean surface, which reasonably agree with the satellite observation by Cyclone Global Navigation Satellite System (CYGNSS) in terms of the phase of the variation. We find that the seasonal variation of the surface particle concentration correlates well with the variation of the mixing layer (ML) depth globally, due to an almost uniform vertical distribution of particles in the ML with total amount of particles conserved.

physics.ao-ph↗

arXiv2022

Phase-resolved ocean wave forecast with simultaneous current estimation through data assimilation

In Wang & Pan (J. Fluid Mech., vol. 918, A19, 2021), the authors developed the first ensemble-based data assimilation (DA) capability for the reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS) method. In this work, we continue to enrich the method by allowing it to simultaneously estimate the ocean current field, which is in general not known a priori and can (slowly) vary in both space and time. To achieve this goal, we incorporate the effect of ocean current (as unknown parameters) on waves to build the HOS-C method as the forward prediction model, and obtain a simultaneous estimation of (current) parameters and (wave) states via an iterative EnKF (IEnKF) method that is necessary to handle the complexity in this DA problem. The new algorithm, named IEnKF-HOS-C method, is first tested in synthetic problems with various forms (steady/unsteady, uniform/non-uniform) of current. It is shown that the IEnKF-HOS-C method is able to not only estimate the current field accurately, but also boost the prediction accuracy of the wave field (even) relative to the state-of-the-art EnKF-HOS method. Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C method successfully recovers the current speed that matches the in situ measurement by a floating buoy.

physics.flu-dyn↗

arXiv2020

Phase-resolved ocean wave forecast with ensemble-based data assimilation

Through ensemble-based data assimilation (DA), we address one of the most notorious difficulties in phase-resolved ocean wave forecast, regarding the deviation of numerical solution from the true surface elevation due to the chaotic nature of and underrepresented physics in the nonlinear wave models. In particular, we develop a coupled approach of the high-order spectral (HOS) method with the ensemble Kalman filter (EnKF), through which the measurement data can be incorporated into the simulation to improve the forecast performance. A unique feature in this coupling is the mismatch between the predictable zone and measurement region, which is accounted for through a special algorithm to modify the analysis equation in EnKF. We test the performance of the new EnKF-HOS method using both synthetic data and real radar measurements. For both cases (though differing in details), it is shown that the new method achieves much higher accuracy than the HOS-only method, and can retain the phase information of an irregular wave field for arbitrarily long forecast time with sequentially assimilated data.

physics.ao-ph↗