Graduate student Aaron Charous from the MSEAS group recently won the Young/Early Career Presenter Award in Computational Acoustics at the 183rd Meeting of the Acoustical Society of America. Aaron’s presentation was on “Learning coordinate transforms for fast and accurate acoustic modeling.” The results enable accurate computational predictions of 3D underwater sound propagation in the ocean with complex bathymetries and discontinuities.

Three awards are made of up to USD $250 each. The award winners were selected based on the quality of the presented paper, comprising both the content and its delivery.

Aaron also won this award at last year’s ASA meeting. Congratulations once again to Aaron!

Reliable underwater acoustic propagation is challenging due to complex ocean dynamics such as internal-waves and to the uncertain larger-scale ocean physics, acoustics, bathymetry, and seabed fields. For accurate acoustic propagation, capturing the important environmental uncertainties and variabilities and predicting the probability distributions of the acoustic pressure field is then what matters. Prior works towards addressing this goal include (i) wave propagation in random media techniques such as perturbation methods, path integral theory, and coupled-mode transport theory, and (ii) probabilistic modeling techniques such as Monte Carlo sampling and Polynomial Chaos expansions. Recently, we developed a novel technique called the Dynamically Orthogonal Parabolic Equations (DO-ParEq) which represent the sound speed, density, bathymetry, and acoustic pressure fields using optimal dynamic Karhunen-Loeve decompositions. The DO-ParEq are range-evolving partial and stochastic differential equations preserving acoustic nonlinearities and non-Gaussian properties. In this presentation, we showcase the theoretical and computational advantages of the DO-ParEq framework compared to the state-of-the-art techniques in the Pekeris waveguide and wedge benchmark problems, in addition to a realistic ocean example in the New York Bight region.

Onboard forecasting and data assimilation are challenging but essential for unmanned autonomous ocean platforms. Due to the numerous operational constraints for these platforms, efficient adaptive reduced-order models (ROMs) are needed. In this thesis, we first review existing approaches and then develop a new adaptive Dynamic Mode Decomposition (DMD)-based, data-driven, reduced-order model framework that provides onboard forecasting and data assimilation capabilities for bandwidth-disadvantaged autonomous ocean platforms. We refer to the new adaptive ROM as the incremental, stochastic Low-Rank Dynamic Mode Decomposition (iLRDMD) algorithm. Given a set of high-fidelity and high-dimensional stochastic forecasts computed in remote centers, this framework enables i) efficient and accurate send and receive of the high-fidelity forecasts, ii) incremental update of the onboard reduced-order model, iii) data-driven onboard forecasting, and iv) onboard ROM data assimilation and learning. We analyze the computational costs for the compression, communications, incremental updates, and onboard forecasts. We evaluate the adaptive ROM using a simple 2D flow behind an island, both as a test case to develop the method, and to investigate the parameter sensitivity and algorithmic design choices. We develop the extension of deterministic iLRDMD to stochastic applications with uncertain ocean forecasts. We then demonstrate the adaptive ROM on more complex ocean fields ranging from univariate 2D, univariate 3D, and multivariate 3D fields from multi-resolution, data-assimilative Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) reanalyses, specifically from the real-time exercises in the Middle Atlantic Bight region. We also highlight our results using the Navy’s Hybrid Coordinate Ocean Model (HYCOM) forecasts in the North Atlantic region. We then apply the adaptive ROM onboard forecasting algorithm to interdisciplinary applications, showcasing adaptive reduced-order forecasts for onboard underwater acoustics computations and forecasts, as well as for exact time-optimal path-planning with autonomous surface vehicles.

For stochastic forecasting and data assimilation onboard the unmanned autonomous ocean platforms, we combine the stochastic ensemble DMD method with the Gaussian Mixture Model – Dynamically Orthogonal equations (GMM-DO) filter. The autonomous platforms can then perform principled Bayesian data assimilation onboard and learn from the limited and gappy ocean observation data and improve onboard estimates. We extend the DMD with the GMM-DO filter further by incorporating incremental DMD algorithms so that the stochastic ensemble DMD model itself is updated with new measurements. To address some of the inefficiencies in the first combination of the stochastic ensemble DMD with the GMM-DO filter, we further introduce the GMM-DMD algorithm. This algorithm not only uses the stochastic ensemble DMD as a computationally efficient forward model, but also employs the existing decomposition to fit the GMM to and perform Bayesian updates on. We demonstrate this incremental stochastic ensemble DMD with GMM-DO and GMMDMD using a real at-sea application in the Middle Atlantic Bight region. We employ a 300 member set of stochastic ensemble forecasts for the “Positioning System for Deep Ocean Navigation – Precision Ocean Interrogation, Navigation, and Timing” (POSYDON-POINT) sea experiment, and highlight the capabilities of reduced data assimilation using simulated twin experiments.

Pat and Marco!!

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His publications so far include:

Z. Sun, Y. Wei, and K. Wu, On Energy Laws and Stability of Runge–Kutta Methods for Linear Seminegative Problems, SIAM Journal on Numerical Analysis, 60(5):2448–2481, 2022. https://epubs.siam.org/doi/10.1137/22M1472218