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Machine Learning for Submesoscale Characterization, Ocean Prediction, and Exploration (ML-SCOPE)

P.F.J. Lermusiaux, P.J. Haley, Jr.,
C.S. Kulkarni, A. Gupta,
C. Mirabito, W.H. Ali, K. Gkirgkis

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

Co-Principal Investigators:
T. Broderick (MIT),
L. Carin (Duke U.),
E.P. Chassignet (FSU),
M.D. Chekroun (UCLA),
S. Jegelka (MIT),
J.C. McWilliams (UCLA),
T.M. Özgökmen (RSMAS - U. Miami)
Project Summary
Ongoing MIT-MSEAS Research
Additional ML-SCOPE Links
MSEAS ML-SCOPE-supported Publications
Background Information

 

This research is sponsored by the Office of Naval Research.

Project Summary

Our long-term goal is to obtain machine-intelligent modeling systems that seamlessly integrate stochastic ocean dynamical models and their multi-fidelity representations with Bayesian and generative learning from data-model misfits, to construct improved ocean models with more accurate parameterizations and discover invariances or differential equations, over a range of spatial and temporal scales. We will use and vastly extend stochastic dynamic reduced-order methods, Bayesian GPs, adaptive DL schemes, and generative networks. Ultimately, our symbolic interpretation of ML models into emergent dynamical and constitutive relations would further compress knowledge relative to deep networks, thus extending outside the range of the training data.

Background information is available below.

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Ongoing MIT-MSEAS Research

Specific Objectives:

Publications

MSEAS ML-SCOPE-supported Publications

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Additional ML-SCOPE Links

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Background Information

The Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) build on years of relocatable ocean modeling experience for physical, acoustical, and biogeochemical studies. The software is used for fundamental research and for realistic simulationsand forecasts of fields and uncertainties around the world’s oceans. Practical applications include ocean monitoring, real-time acoustic predictions and DA, biogeochemical-ecosystem predictions and environmental management, relocatable rapid response, path planning for autonomous vehicles, and adaptive sampling. MSEAS has been tested and validated in a wide range of real-time forecasting exercises. They include: AWACS and SW-06, AOSN-II and MB-06, QPE-08 and -09, PhilEx-08 and -09, and NASCar and FLEAT. Recently, we issued multi-resolution forecasts of 3D Lagrangian transports, coherent structures, and their uncertainties, and guided drifter releases for optimal Lagrangian sampling (NSF-ALPHA). Using ESSE, we used large-ensemble forecasts of high-resolution fields (sound-speed, currents, etc.) for 3D underwater-GPS exercises (POINT). MSEAS also includes finite-element codes for non-hydrostatic dynamics as well as a framework for stochastic 2D ocean and fluid flows and incubation of DA and ML methods. Our proposed research will leverage all of these efforts.

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