Pierre Lermusiaux

Massachusetts Institute of Technology
Department of Mechanical Engineering
Room 5-207B, 77 Massachusetts Avenue
Cambridge, MA 02139-4301

Tel: 617-324-5172
Fax: 617-324-3541
Email: pierrel at mit.edu

Group Web: http://mseas.mit.edu/

Recent Papers
Complete Set of Papers


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.


Present Research Projects

Machine Learning Downscaling Capability for Environmental Forecasts
Researching Interior Ocean Trajectories: Sensing, Quantifying, Utilizing, and Adapting to Dynamics (RIOT - SQUAD)
Advancing Gulf of Mexico Operational Forecasting with Application to Fisheries, Industry Safety, and Natural Hazards (GOFFISH)
Machine Learning for Submesoscale Characterization, Ocean Prediction, and Exploration (ML-SCOPE)
Dynamic Environmental Estimation, Prediction, and Acoustic Inference (DEEP-AI)
Interdisciplinary Nonlinear Bayesian Data Assimilation

Publications

Uncertainty Quantification and Reduced-Order Modeling
Learning and Data Assimilation
Science of Autonomy
Applications to Ocean Dynamics
Numerical Modeling
Scientific Visualization

General Press
MIT MechE Theses

Recent Sea Exercises

RIOT IOP26
GRand Adaptive Sampling Experiment (GRASE) 2025
New England Seamounts Experiment (NESMA) 2024
MASTR Real-Time Sea Experiment 2024
CALYPSO 2022

Recent Presentations, Experiments, etc.

Lermusiaux, P.F.J., P.J. Haley Jr., and C. Mirabito, 2026. Lagrangian Flow Map Analysis of Submesoscale Dynamics. In: 57th International Liège Colloquium on Ocean Dynamics, Liège, 25–29 May 2026.
Lermusiaux, P., P.J. Haley Jr., and C. Mirabito, 2026. Lagrangian Flow Map Analysis of Material Transports and Ocean Dynamics. In: AGU Ocean Sciences Meeting, Glasgow, 22–27 February 2026.
Lermusiaux P.F.J., P.J. Haley, Jr., and C. Mirabito, 2026. MIT-MSEAS Updates for the Annual RIOT Research Planning Meeting. Annual RIOT Meeting. Portland, OR. January 15, 2026.
Lermusiaux P.F.J. and the MSEAS group, 2026. Bayesian Learning of Reactive Fluid Dynamical Models. Invited Panelist at the “Data-Driven Modeling of Combustion Dynamics” Panel Session at the AIAA SciTech 2026 Forum (https://aiaa.org/scitech/), Orlando, Florida, USA

Recent Invited Lectures
Press Accounts of Group Activities
Real-Time Forecasting and Data Assimilation Experiments
Various Links of Interest

Past Research Projects

Biological Noise Modeling for Active and Passive Sonar System Performance Predictions
Affordable Gigaton-scale Carbon Sequestration
Compression and Assimilation for Resource-Limited Operations
K2D: Knowledge and Data from the Deep to Space
Intelligent Observing and Multiscale Modeling for Ocean Exploration and Sustainable Utilization
Local Stochastic Prediction for UUV/USV Environmental Awareness – ROMs
The Potential Effects of Deep Sea Mining on Deep Midwater Communities in the CCZ: Constructing Ecosystem Baselines and Modeling Effects on Ecosystem Services
Autonomous Tow Vessels for Offshore Macroalgae Farming
Plastic Pollution in the Oceans: Characterization and Modeling
Wide Area Ocean Floor Mapping
Deep Sea Mining: Modeling, Observing and Quantifying Risks
Bayesian Intelligent Ocean Modeling and Acidification Prediction Systems (BIOMAPS)
Understanding and Predicting the Gulf of Mexico Loop Current
Coherent Lagrangian Pathways from the Surface Ocean to Interior (CALYPSO)