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One of our research thrusts is to derive and apply advanced techniques for multiscale modeling of tidal-to-mesoscale processes over regional domains (nearshore-coastal-basin) with complex geometries including shallow seas with strong tides, steep shelfbreaks with fronts, and deep ocean interactions. On the one hand, our conservative implicit two-way nesting for realistic multi-resolution modeling has enabled such high-fidelity coupled multiscale dynamics studies. On the other hand, a high-order multi-dynamics modeling capability based on novel hybridizable discontinuous Galerkin (HDG) numerical schemes is also promising for seamless conservative multi-resolution forecasting. These two research topics are the backbones of our National Oceanographic Partnership Program (NOPP) research project.
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. First, the processes involved in these strong topographic interactions are not yet well known. Their consequences, including alteration of circulation features, spawning of internal waves and vortices, and formation of unstable downslope flows and gravity currents, require novel integrated analyses. Second, major ocean ridges and archipelagos and islands are not properly represented in larger-scale modeling systems, and novel downscaling and two-way nesting schemes need to be utilized, developed and evaluated with real ocean data. This set of research activities is the emphasis of our FLEAT research project.
Today, the number of autonomous platforms used in semi-coordinated sea operations can be larger than 10 and this number is increasing. This new paradigm in ocean science and operations calls for investigations as those envisioned by the Northern Arabian Sea Circulation – autonomous research (NASCar) initiative. The need for clever autonomous observing and prediction systems is especially acute in the NASCar region due to the frequent pirate activities and the relative paucity of in situ observations.
In the ocean domain, opportunities for a paradigm shift in the science of autonomy involve fundamental theory, rigorous methods and efficient computations for autonomous systems that collect information, learn, collaborate and make decisions under uncertainty, all in optimal integrated fashion and over long duration, persistently adapting to and utilizing the ocean environment. The corresponding basic research is the emphasis of the present project.As humans, we often combine what we learn over time, and then make informed decisions and complete desired and new tasks. The learning over time, or backward and forward inference, is critical for long-term autonomy, especially in complex nonlinear settings that are ubiquitous in the ocean domain. Mathematically, all initial and acquired information, from both models and data, should be integrated in the form of posterior marginals of the variables of interest, while respecting nonlinearities. The accurate posterior probabilities then facilitate persistent learning, metacognition, informed decisions and tasks completion under uncertainty. Such rigorous nonlinear time-space integration of information and learning, and its application to sustained coordinated autonomous operations of multiple collaborative vehicles is a major focus of the present research. Challenges in our ocean domain arise due to the: complex nonlinear multiscale, multivariate ocean dynamics; large-dimension of the autonomy problems over long duration and large spatial extent; sparse, gappy and multivariate measurements; autonomous coordination and collaboration among heterogeneous vehicles into efficient swarms; and, integration of multiple disciplines into environmentally-adaptive, autonomous and rigorous naval systems.
Our long-term goal is to develop and apply new theory, algorithms and computational systems for the sustained coordinated operation of multiple collaborative autonomous vehicles over long time durations in realistic multiscale nonlinear ocean settings such that the integrated naval system optimally collects observations, rigorously propagates information backward and forward in time, and accurately completes persistent learning, environmental adaptation, machine metacognition and decision making under uncertainty.
Studies involve the coupling of ocean-acoustic models in 4D, using a hierarchy of acoustic codes, in collaboration with MIT and other PIs. The specific research tasks include:
The overall objective of this program is to conduct basic research that will help enable robust autonomy and automation in dynamic, unconstrained environments and contexts. The two science problems of interest are how a learning machine may leverage all relevant prior knowledge and how it may leverage occasional in situ availability of a subject matter expert (SME). This leads naturally to the existing research of Transfer Learning and Active Learning. The intent of the ATL program is to improve upon these two existing areas of research and combine them to produce a novel, powerful learning capability. The primary deficiency of active learning is that it typically involves only labeling exemplars and does not allow the SME to fully impart his/her rich domain knowledge as they would to a human student. A deficiency of transfer learning is that when it fails it is typically not possible for an SME to repair or complete the transfer in situ. By exploiting both of these deficiencies, this ATL program seeks to fundamentally extend the scope of active learning and incorporate it into the knowledge transfer process. The two specific technical goals are to capitalize on the occasional availability of an SME to enable: 1) the robust transfer of knowledge from existing sources; and, 2) the injection of new knowledge in situ. This first technical goal includes both machine-initiated and human-guided exploration of existing knowledge sources as well as machine-based reasoning on knowledge sufficiency for prompting SME queries. This second technical goal includes both machine-initiated queries of target knowledge as well as SME injection of new, rich domain knowledge into the target.
A main focus of this research is to determine the role of stochastic forcing on ocean uncertainty and variability predictions. The work includes collaborations with NRL-Stennis to prepare the transfer of a subset of the capabilities and software developed by the MSEAS group. This applied research in stochastic modeling and ocean uncertainty prediction is linked to two growing fundamental fields: prediction and reduction of uncertainties; and, estimation of properties by combining models with data. From a fundamental viewpoint, uncertainty is characterized by a probability density function (pdf). One of the aims of the applied research and collaborations with NRL will be to improve the prediction of such pdfs.
The research thrusts for this effort include:
Our specific objectives are to:
As part of the NSF Ocean Observatories Initiative (OOI), the oceanographic community is deploying a facility called the “Pioneer Array” tethered to the bottom of the ocean off the New England Coast. It is the first community relocatable process-oriented observatory, the equivalent of an international cyclotron for coastal ocean science. The array will enable a flotilla of measurement devices of different types to provide a rich data stream of physical and biological processes in the vicinity of the shelf-break front. This is a pilot project to put in place the multi-scale computational fluid dynamical modeling infrastructure required to make best use of the observations to be collected.
The pilot project will seed an ocean modeling collaboration that will exploit opportunities provided by the Pioneer Array for longer term research and educational activities in the MIT, UMass and WHOI communities and around the region. We are supporting two activities:
Today, the number of autonomous platforms used in semi-coordinated operations is often larger than 10 and this number is rapidly increasing. It is imperative to advance the interdisciplinary science of autonomy to take full advantage of these new capabilities and so maintain our leading scientific and engineering edge. We are researching novel fundamental formalisms and principled methodologies for optimal sensing using heterogeneous and collaborative swarms of autonomous platforms that are smart. Intelligence here refers to the ability to compute and autonomously adapt an optimal sensing plan based on the predicted environment and acoustic performance and their uncertainties, and on the predicted effects of environmental and acoustic sensing on future operations. Our approach is generic and applicable to any Naval swarms that move and sense large-dimension dynamics fields, but our focus is underwater sensing with a range of platforms including AUVs, gliders, ships, moorings and remote sensing.
When compared to other control problems of large dimensions, the differences with our problem are that: (i) naval platforms are heterogeneous and their data are gappy but multivariate; (ii) marine fields are dynamic on multiple-scales and have very large dimensions, but are predictable to some degree; and (iii), the measurements to be collected will affect these future predictions. Therefore, there are feedbacks between optimal sensing and predicting, in time and space, and across variables. We use guidance from ocean-acoustic modeling, dynamical system theory, uncertainty prediction, decision-making under uncertainty, artificial intelligence, bio-inspired algorithms with emergence of global properties, and distributed computing. In all cases, we are interested in the global swarms and high-level optimization, not the detailed control of a single robot. A global objective function defines the optimal dynamic and collaborative autonomy. In our case, objective functions depend on the predicted environment, on the predicted values and positions of the expected data, and on the feedbacks between data and dynamics.
We are researching autonomous sensing swarms and formations that exploit the multi-scale, multivariate, four-dimensional environmental-acoustic dynamics and predictabilities. We are developing new global swarm patterns and high-level optimization schemes based on control theory and dynamical system theory (e.g. artificial potential functions, nonlinear contraction analysis), artificial intelligence (e.g. hybrid evolutionary optimization, particle swarm optimization and reinforcement learning) and bio-inspired behaviors (e.g. distributed flocking/swarming, ant algorithms). From the methods explored, a small subset of candidate schemes are being selected based on accuracy, robustness, predictability and generality. These selected schemes will be augmented to learn from (i) environmental forecasts and their associated uncertainties and (ii) the projected impact of the sensing on the forecasts. Incremental testing of the algorithms will be accomplished using idealized simulations. System tests use hindcasts from our extensive set of at-sea exercises.
The Ocean Observatories Initiative (OOI) is a NSF Division of Ocean Sciences program that focuses the science, technology, education and outreach of an emerging network of science driven ocean observing systems. The core capabilities and the principal objectives of ocean observatories are collecting real-time data, analyzing data and modeling the ocean on multiple scales, and enabling adaptive experimentation within the ocean.
OOI CyberInfrastructure (CI) conducted an Observing System Simulation Experiment (OSSE) to test the capabilities of the OOI CI to support field efforts in a distributed ocean observatory in the Mid-Atlantic Bight. The goal was to provide a real oceanographic test bed in which the CI will support field operations of ships and mobile platforms, aggregate data from fixed platforms, shore-based radars, and satellites and offer these data streams to data assimilative forecast models. The MAB region was selected because of the existing communities and the presence of NOAA, ONR coordinated by Oscar Schofield in the context of the MARCOOS effort. The experiment took place October-December 2009.
The MSEAS group and the MIT Laboratory for Autonomous Marine Sensing Systems (LAMSS) group supported the OSSE through the integration of MOOSDB and MOOS-IvP components, behaviors and autonomous platform systems with ocean modeling and forecasting, data assimilation and uncertainty estimation, and adaptive sampling. The integrated OSSE prototype was developed in a simulator environment which allowed for the testing of glider and AUV parameters in the MSEAS simulated ocean conditions. Selected configurable MOOS-IvP behaviors were provided suitable for adaptive observations in OSSE scenarios utilizing available real-time numerical model output and CASPER execution.
This research is concerned with the fundamental understanding and modeling of complex physical, acoustical and biogeochemical oceanic dynamics and processes. New mathematical models and computational methods are created, developed and utilized for: (i) ocean predictions and dynamical diagnostics, (ii) data assimilation and data-model comparisons, and, (iii) optimization and control of autonomous ocean observation systems. The regional dynamics involves interactions of sub-mesoscale and mesoscale ocean processes in the littoral as well as effects from large-scale processes in ocean basins. Such interactions and feedbacks with scales smaller and larger than the mesoscale need be better quantified. The technical approach is rooted in the comparison and optimal combination of measurements and models via nonlinear data assimilation (DA), including the development of adaptive modeling and adaptive sampling schemes based on Error Subspace Statistical Estimation. Our research group is updating and renewing our previous approaches and computational schemes and systems. We will keep and modernize the strengths of our methods and codes, but we will also progressively utilize other ocean dynamical models, or parts thereof, and explore novel numerical systems.
The research topics specific to this effort include: (i) three-dimensional (3D) acoustic modeling coupled with high-resolution 4D physics modeling; (ii) ocean modeling incubator: structured and unstructured grids; investigations and evaluations of the next generation of numerical schemes for physical, acoustical and biological dynamics; (iii) interactions of internal tides/waves and mixing processes with mesoscale dynamics, their high-resolution modeling and multi-scale diagnostics; (iv) Lagrangian coherent structures and ocean features: their prediction, dynamics and assimilation; (v) nonlinear DA and adaptive DA, including (super)-tidal constraints and assimilation; and, (vi) use of several ocean models, model uncertainty estimation, and multi-model fusion and DA.
General objectives are to: (i) analyze and study regional physical and acoustical-physical-biogeochemical dynamics; (ii) incubate and develop new numerical modeling systems, including next generation ocean physics, 3D acoustics and Lagrangian coherent structures predictions; (iii) update existing and create new nonlinear and adaptive assimilation schemes and systems, including parameter estimation; (iv) evolve concepts and determine methodologies for regional adaptive modeling and adaptive sampling with the intent to increase predictive capabilities; (v) quantify regional predictabilities and improve probability and uncertainty modeling; and, (vi) utilize several ocean models, estimate their uncertainty statistics and fuse their estimates.
An emphasis is on acoustical-physical interactions in 3D space and time, and on acoustical-biogeochemical-physical estimation. The investigations are generic but the focus is on specific ocean regions: the Mid-Atlantic Bight (MAB) and Shelfbreak Front region, the Chinese-Taiwanese Seas and Philippine Seas; the Monterey Bay and California Current System (CCS) region, the Massachusetts Bay/New England shelf region, and the Mediterranean and Black Seas. Several of these regions have been or are investigated under other collaborative efforts, some of which sponsored by the Office of Naval Research.
The long-term goal is to: research, integrate, demonstrate and utilize end-to-end prediction and DA systems to better study, understand, forecast and exploit environmental and acoustic fields and uncertainties for efficient sonar operations.
Specific objectives are to:
End-to-end Prediction and DA Systems and their Uncertainties. An important component of this work involves the research, integration, demonstration and utilization of end-to-end prediction and DA systems for efficient sonar operations. Our research collaborations include the following components of such systems: ocean physics models (the free-surface model of MSEAS), acoustic models (NPS model and RAM), coupling schemes for these water-column and acoustic models, and the corresponding DA systems. We have extended our modeling experience and ESSE data assimilation system to seabed and signal-to-noise-ratio (SNR) modeling and assimilation, for fully coupled ocean-physics-acoustic-seabed-SNR estimations. The accounting of all system uncertainties including those of the ocean and bottom environment, and of the sonar equations, must to be accurate enough for successful end-to-end estimations (Lermusiaux, 2006a). The uncertainty estimates computed by the DA systems are evaluated by statistical analyses and comparison to data-forecast misfits. Interesting research has involved the theoretical modeling and estimation of uncertainties for idealized systems. Such idealized research has been necessary for determining the accurate representation and transfer of uncertainties across the various disciplines.
Ocean Dynamics, Features and Predictability. We are studying, modeling and quantifying ocean dynamics and features in the East China Sea (ECS) and Northern Philippine Sea region, with emphases on oceanic events that are acoustically important. Processes of interests include interactions of the meandering Kuroshio with shelf dynamics and topographic features (entrainment, encircling of ECS waters, eddying, etc) and interactions of mesoscales with internal tides and waves in the ECS.
This research is concerned with interdisciplinary modeling, data assimilation and dynamical studies in the Straits regions of the Philippines Archipelago. The general focus is to better understand, model and predict sub-mesoscale and mesoscale physical and biogeochemical dynamics in sea straits. The technical approach is based on interdisciplinary data assimilation using the Error Subspace Statistical Estimation scheme, quantitative model evaluation and selection through adaptive modeling, and sensitivity and dynamical process studies. The work and its results are expected to contribute to navy operations including the surveillance of transit routes, safety of man-based activities, management of autonomous vehicles, and overall tactical and strategic decision making under uncertainties in sensitive regions.
Specific objectives are to:
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