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Our research vision is to develop and transform ocean modeling and data assimilation to quantify regional ocean dynamics on multiple scales. Our group creates and utilizes new models and methods for multiscale modeling, uncertainty quantification, data assimilation and the guidance of autonomous vehicles. We then apply these advances to better understand physical, acoustical and biological interactions. Our environment is collaborative within a lively group of students and researchers. We seek both fundamental and applied contributions to build knowledge and benefit society.

Physical and Interdisciplinary Regional Ocean Dynamics and Modeling Systems

Our research includes: quantifying of ocean features and dynamics, multi-scale numerical modeling, and uncertainty quantification and assimilation schemes. Recent results include high-order hybrid Finite-Element schemes for physical-biological dynamics at shelfbreaks (bottom left), rigorous nonlinear and non-Gaussian data assimilation using our GMM-DO filter and smoother (bottom right) and exact path planning for swarms of underwater vehicles using level-set equations (top right).

 

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

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.

 

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

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, (ii) Assimilate the sparse acoustic and oceanographic data, (iii) Learn and discover acoustic parameterizations, model improvements, new processes, and most informative observation needs, and (iv) Develop efficient computational methods for the above prediction, assimilation, and learning.

 

Interdisciplinary Nonlinear Bayesian Data Assimilation

Our long-term goal is to: generalize, develop, and implement our stochastic dynamically-orthogonal decompositions and nonlinear Bayesian filtering and smoothing schemes for principled probabilistic predictions and predictability studies of physical-acoustical-biogeochemical-sea-ice dynamics, and for interdisciplinary nonlinear Bayesian data assimilation, adaptive sampling, and quantification of observation needs for naval operations.

 

Local Stochastic Prediction for UUV/USV Environmental Awareness for Applied Ocean Sciences

We plan to collaborate with Applied Ocean Sciences (AOS) to help designing and delivering a compact system to assess local uncertainties and track the evolution of the maritime environment around unmanned platforms at sea. Such a system can run both at control centers and on-board Underwater and Surface Unmanned Vehicles (UUV/SUV) under different network bandwidth constraints. The system uses the Navy ocean forecasts for initial environmental guesses and outlooks up to 2 weeks (or more in future generations) and then implements a Reduced Order Model (ROM) derived from Dynamically Orthogonal (DO) solutions to deliver a local uncertainty picture (for the next 24-48 hours).

 

Autonomous Tow Vessels for Offshore Macroalgae Farming

The matter of ocean forecasting and route planning is an important aspect of maximizing the value of the Autonomous Tow Vessels for Offshore Macroalgae Farming developed C.A. Goudey & Associates. For this challenging project, we apply and further develop our experience with the optimization of vehicle trajectories in dynamic flow fields and in forecasting such flows and optimal trajectories. Our MSEAS group research has an emphasis on the following support: (1) Examine the importance of route optimization in Drone Tug applications in local tidal and coastal flows based on speed and maneuvering abilities; (2) Consultations on Drone Tug evaluation and applications with a focus on operations in the vicinity of Woods Hole and Nantucket Sound; (3) Support of Drone Tug demonstrations at Nantucket Sound kelp farm.

 

Plastic Pollution in the Oceans: Characterization and Modeling

Since the 19th and early-20th century, plastics have become ubiquitous in the world. Plastics have outgrown most man-made materials: their global volume production has surpassed that of steel production in the late 1980s (Fernandez et al., 2018). The MIT Environmental Solutions Initiative (MIT-ESI) aims to tackle this challenge through the expertise of our interdisciplinary faculty, ranging from materials, to manufacturing and design, to smart sensing and advanced computational modeling and data-driven learning. Through our approaches and collaborations, our long-term goal is to develop a plastic free environment that will ensure the health of our planet.

 

Bayesian Intelligent Ocean Modeling and Acidification Prediction Systems (BIOMAPS)

The overarching goal of this project is to develop and demonstrate principled Bayesian intelligent ocean modeling and acidification prediction systems that discriminate among and infer new ocean acidification (OA) models, rigorously learning from data-model misfits and accounting for uncertainties, so as to better monitor, predict, and characterize OA over time-scales of days to months in the Massachusetts Bay and Stellwagen Bank region.

 

Wide Area Ocean Floor Mapping

Our primary goal is to further develop, implement, apply, and validate theory and schemes for a prototype Wide Area Ocean Floor Mapping system. Our specific objectives are to: (i) Characterize and predict underwater sound propagation uncertainty/distributions due to inexact bathymetry fields and ocean environmental and seabed uncertainties, and (ii) assimilate limited acoustic and oceanographic data for the joint principled Bayesian inversion of environmental and acoustical fields, and for the corresponding rigorous estimation of bathymetry.

 

Bayesian Data Assimilative Ocean Forecasting, Learning, and Optimal Sensing for Sustainable Fisheries Management in India

This project will develop Bayesian data-driven estimation and model learning methods, stochastic forecasts, and analysis products for ocean physics, biogeochemistry and fisheries. Our data-assimilative ocean field and uncertainty estimates, optimal data collection guidance, and coastal ecosystem-based scenario and risk analyses will serve as quantitative technical decision aides for sustainable rights-based fisheries management. Our forecasts have diverse local commercial and societal applications involving not only fisheries but also coastal zone management, pollution mitigation, monitoring, ocean engineering, tourism, shipping, financial hedging, and re-insurance.

 

Understanding and Predicting the Gulf of Mexico Loop Current

The overarching goal of this collaborative project is to achieve greater understanding of the physical processes that control the circulation in the Gulf of Mexico, in particular the Loop Current and Loop Current eddy separation dynamics, through advanced data assimilative modeling and analyses. Our MSEAS group plans to: contribute to the skill assessment and analysis of the operational modeling systems; complete targeted multi-resolution modeling experiments to study the effect of model resolution, initial and boundary conditions; illustrate our capabilities of probabilistic predictability and process analyses; and, predict the information content and impacts of observations for principled observational campaign design.

 

Coherent Lagrangian Pathways from the Surface Ocean to Interior (CALYPSO)

Describing and quantifying the truly three-dimensional and time-dependent transports of ocean properties from the surface ocean to the interior is a fascinating observational, theoretical, and modeling challenge. The CALYPSO initiative addresses this challenge, with a focus on the southwest Mediterranean Sea region. Our goal is to develop novel efficient four-dimensional Lagrangian analysis theory and methods, and apply and expand our capabilities in multi-resolution multi-disciplinary ocean modeling, uncertainty, predictability, and Lagrangian–Eulerian data assimilation, to predict and characterize multiscale ocean transports, coherent structures, and subduction/stirring/mixing processes, and optimally guide ocean platforms towards the most informative observations.

 

Surface Dynamic Uncertainty Characterization and Transfer

(S-DUCT)

The long-term MIT-MSEAS goals of the S-DUCT effort are to (i) employ and develop our high-resolution MSEAS modeling system in ocean regimes with surface ducts, (ii) develop and utilize our coupled oceanographic-acoustic probabilistic modeling, (iii) employ and advance our coupled Bayesian data assimilation (GMM-DO filter and smoother), (iv) apply our theory and schemes based on Bayesian mutual information fields, and (v) quantify the sound speed and transmission loss variability in surface duct regions and investigate models of such effects that are useful for naval applications.

 

Red Sea Initiative

Our research focus will be the Lagrangian connectivity of marine ecosystems and sea hazards due to natural or made-made Lagrangian material transports. Specifically, we plan to further develop and apply our new theory and schemes for (i) the study and quantification of biogeochemical coherent structures and Lagrangian connectivity of marine ecosystems, and (ii) the study and mitigation of sea hazards due to stochastic advection and Lagrangian material transports including marine contaminations and spills.

 

Advanced Lagrangian Predictions for Hazards Assessments (NSF-ALPHA)

The overall goal of the ALPHA project is to employ an integrated theoretical, computational and observational approach to develop, implement and utilize these cutting-edge Lagrangian methods with data-driven modeling for the purpose of uncovering, quantifying and predicting key transport processes and four-dimensional (3D plus time) structures during regional flow-based hazards in the ocean and atmosphere.

 

High-Order Multi-Resolution Multi-Dynamics Modeling for the Flow Encountering Abrupt Topography (FLEAT) Initiative

The presence of large gradients often renders the quantitative analysis of dynamical systems challenging, be the analysis theoretical, observational or computational. This is because large gradients commonly lead to strong nonlinearities and to coupling among state variables and parameters. The emphasis of the Flow Encountering Abrupt Topography (FLEAT) initiative is on the effects of large topographic gradients and complex subsurface geometry on major current systems. The processes involved in these strong topographic interactions and their consequences are the emphasis of our FLEAT research project.