23 citations · 53 across the 10 of their papers we have counts for
12 papers
Modified Dynamic Mixed Subgrid-scale Models for Geophysical Flows: Forced Two-Dimensional and -plane Turbulence
Anantha Narayanan Suresh Babu, Akhil Sadam, Pierre F. J. Lermusiaux
Subgrid-scale (SGS) models for large-eddy simulations (LES) of geophysical turbulence typically need to balance dissipative regularization with backscatter, the upscale transfer of…
Evaluation of Deep Neural Operator Models toward Ocean Forecasting
Ellery Rajagopal, Anantha N. S. Babu, Tony Ryu +3
Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains includi…
Stranding Risk for Underactuated Vessels in Complex Ocean Currents: Analysis and Controllers
Andreas Doering, Marius Wiggert, Hanna Krasowski +3
Low-propulsion vessels can take advantage of powerful ocean currents to navigate towards a destination. Recent results demonstrated that vessels can reach their destination with hi…
Maximizing Seaweed Growth on Autonomous Farms: A Dynamic Programming Approach for Underactuated Systems Navigating on Uncertain Ocean Currents
Matthias Killer, Marius Wiggert, Hanna Krasowski +3
Seaweed biomass presents a substantial opportunity for climate mitigation, yet to realize its potential, farming must be expanded to the vast open oceans. However, in the open ocea…
Generalized Neural Closure Models with Interpretability
Abhinav Gupta, Pierre F. J. Lermusiaux
Improving the predictive capability and computational cost of dynamical models is often at the heart of augmenting computational physics with machine learning (ML). However, most l…
Stable rank-adaptive Dynamically Orthogonal Runge-Kutta schemes
Aaron Charous, Pierre F. J. Lermusiaux
We develop two new sets of stable, rank-adaptive Dynamically Orthogonal Runge-Kutta (DORK) schemes that capture the high-order curvature of the nonlinear low-rank manifold. The DOR…