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).
Seamless Multiscale Forecasting: Hybridizable Unstructured-mesh Modeling and Conservative Two-way Nesting
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 high-fidelity studies of coupled multiscale ocean dynamics. 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 NOPP research project.
Northern Arabian Sea Circulation – autonomous research: Optimal Planning Systems (NASCar-OPS)
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.
Bayesian Nonlinear Assimilation of Eulerian and Lagrangian Coastal Flow Data
The long-term goal is to develop and apply theory, schemes and computational systems for rigorous Bayesian nonlinear assimilation of Eulerian and Lagrangian coastal flow data, fully exploiting nonlinear governing equations and mutual information structures inherent to coastal ocean dynamical systems and optimally inferring multiscale coastal ocean fields for quantitative scientific studies and efficient naval operations. The motivation is to exploit the information provided by coastal platforms (drifters, floats, gliders, AUVs or HF-radars) so as to best augment the limited resolution and accuracy of satellite data in coastal regions and to determine coastal sampling needs for successful Bayesian field estimation in diverse coastal regimes.
Long-duration Environmentally-adaptive Autonomous Rigorous Naval Systems (LEARNS)
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 LEARNS project.
Integrated Ocean Dynamics and Acoustics (IODA)
The goal is multi-resolution data-assimilative modeling to study truly multiscale coastal ocean dynamics and their acoustic effects, with an emphasis on resolving internal tides and long nonlinear internal waves and their interactions with the real ocean, including: All coastline, shelf, shelfbreak and deep ocean features; high-resolution steep bathymetry; and, atmospheric fluxes as external forcing; and, Stochastic parameterizations of sub-grid scales (nonlinear internal waves and other effects) for 4D hydrostatics models, and new non-hydrostatic HDG scheme in idealized conditions.
Active Transfer Learning for Ocean Modeling (ATL)
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.
Causes and Effects of Shelf-edge Internal Tide Variability
Internal tide generation and propagation near continental slopes are being studied using a four-dimensional numerical simulation and diagnosis approach. The purpose is to explain observed variability in internal tides and the nonlinear waves they spawn. The study is concentrating on long wavelength linear internal waves (internal tides) generated from subcritical tidal flow (current speed less than wave speed), ubiquitous around the world. Three internal tide effects are being examined: variable generation, heterogeneous propagation (i.e. focusing), and conversion to nonlinear waveform.
High Productivity on a Coastal Bank: Physical and Biological Interactions
Stellwagen Bank supports a multiplicity of life forms, from plankton to whales. This project seeks to mathematically model and investigate the interplay among physical and biological processes that support the productivity of the Bank’s ecosystem. The multiscale MSEAS modeling system is being used to investigate the roles of physical features and processes in distributing nutrients and thus the production and retention of phytoplankton biomass.
Autonomous Marine Intelligent Swarming Systems for Interdisciplinary Observing Networks (A-MISSION)
We research autonomous sensing swarms and formations that exploit the multi-scale, multivariate, four-dimensional environmental-acoustic dynamics and predictabilities. We develop 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).