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Dynamic Environmental Estimation, Prediction, and Acoustic Inference (DEEP-AI)

P.F.J. Lermusiaux, P.J. Haley, Jr.,
C. Mirabito, M.S. Bhabra, W.H. Ali,
M. Humara, A. Charous

Massachusetts Institute of Technology
Center for Ocean Engineering
Mechanical Engineering
Cambridge, Massachusetts

Project Summary
Ongoing MIT-MSEAS Research
Additional DEEP-AI Links
MSEAS DEEP-AI-supported Publications
Background Information

 

This research is sponsored by the Office of Naval Research.

Project Summary

The main goal for our project is to further develop, implement, apply, and validate theory, algorithms, and computational schemes for dynamic environmental estimation, prediction, and acoustic inference (DEEP-AI). The specific research thrusts are to: (i) Predict and characterize underwater sound propagation PDFs due to the uncertain ocean oceanographic, bathymetry, and seabed fields, (ii) Assimilate the sparse acoustic and oceanographic data using multivariate principled Bayesian inversion and estimation of ocean oceanographic, acoustic, bathymetry, and seabed fields, (iii) Learn and discover acoustic parameterizations, model improvements, new processes, and most informative observation needs using new deep machine learning and Bayesian learning, and (iv) Develop efficient computational methods for the above prediction, assimilation, and learning.

Background information is available below.

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Ongoing MIT-MSEAS Research

Specific Objectives:

Publications

MSEAS DEEP-AI-supported Publications

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Additional DEEP-AI Links

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Background Information

Our research builds on years of experience in multidisciplinary fields. The MSEAS software (MSEAS Group, 2010, Haley et al., 2015) has been used for fundamental research and for simulations and forecasts of fields and uncertainties in many regions of the world's ocean (Lermusiaux et al., 2006; Leslie et al, 2008; Onken et al, 2003, 2008; Haley et al, 2009; Ramp et al., 2011; Gangopadhyay et al., 2011; Colin et al., 2013; Kelly and Lermusiaux, 2016; Lermusiaux et al., 2011, 2017a,b). Applications include: ocean monitoring (Lermusiaux et al., 2007); real-time acoustic predictions (Xu et al., 2008; Lam et al., 2009; Lermusiaux et al., 2010; Duda et al., 2011); biogeochemical-ecosystem predictions and environmental management (Besiktepe et al., 2003; Cossarini et al., 2009; Coulin et al., 2017); relocatable rapid response (e.g. Rixen et al., 2012; De Dominicis et al., 2014); planning for underwater vehicles (Schofield et al., 2010; Lolla et al., 2014a,b; Lermusiaux et al., 2016); and, adaptive sampling (Lermusiaux, 2007; Heaney et al.,2007, 2016). MSEAS has been validated in a wide range of real-time forecasting exercises. They include: AWACS and SW-06 (Haley and Lermusiaux, 2010; Colin et al., 2013); AOSN-II and MB-06 (Lermusiaux et al., 2006, Gangopadhyay et al., 2011; Ramp et al., 2011); QPE-08 and -09 (Lermusiaux et al., 2010; Gawarkiewicz et al., 2011; Lermusiaux et al., 2019); PhilEx-08 and -09 (Agarwal and Lermusiaux, 2011; Lermusiaux et al., 2011); and NASCar and FLEAT (Lermusiaux et al., 2017a,b; Pan et al., 2018). Recent exercises include Lagrangian transport studies for NSF-ALPHA and probabilistic ocean forecasting for 3D underwater positioning (DARPA-POINT).

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