75 citations · 182 across the 12 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…
Bayesian Learning of Coupled Biogeochemical-Physical Models
Abhinav Gupta, Pierre F. J. Lermusiaux
Predictive dynamical models for marine ecosystems are used for a variety of needs. Due to sparse measurements and limited understanding of the myriad of ocean processes, there is h…
Deep Reinforcement Learning for Adaptive Mesh Refinement
Corbin Foucart, Aaron Charous, Pierre F. J. Lermusiaux
Finite element discretizations of problems in computational physics often rely on adaptive mesh refinement (AMR) to preferentially resolve regions containing important features dur…
Lagrangian surface signatures reveal upper-ocean vertical displacement conduits near oceanic density fronts
H. M. Aravind, Vicky Verma, Sutanu Sarkar +5
Vertical transport in the ocean plays a critical role in the exchange of freshwater, heat, nutrients, and other biogeochemical tracers. While there are situations where vertical fl…
Neural Closure Models for Dynamical Systems
Abhinav Gupta, Pierre F. J. Lermusiaux
Complex dynamical systems are used for predictions in many domains. Because of computational costs, models are truncated, coarsened, or aggregated. As the neglected and unresolved…
Search and rescue at sea aided by hidden flow structures
Mattia Serra, Pratik Sathe, Irina Rypina +6
Every year hundreds of people die at sea because of vessel and airplane accidents. A key challenge in reducing the number of these fatalities is to make Search and Rescue (SAR) alg…
A Geometric Approach to Dynamical Model-Order Reduction
Florian Feppon, Pierre F. J. Lermusiaux
Any model order reduced dynamical system that evolves a modal decomposition to approximate the discretized solution of a stochastic PDE can be related to a vector field tangent to…