Dynamic Environmental Estimation, Prediction, and Acoustic Inference (DEEP-AI)
Abstract
The main goal is to further develop, implement, apply, and validate theory, algorithms, and computational schemes for dynamic envir"onmental estimation, prediction, and acoustic inference (DEEP-AI). The research thrusts are to: i) Predict and characterize underwater acoustic probability density functions due to the uncertain ocean oceanographic, bathymetry, and seabed fields; ii) Assimilate the sparse acoustic and oceanographic data using multivariate principled Bayesian inversion and estimation of oceanographic, acoustic, bathymetry, and seabed fields; iii) Learn and discover acoustic parameterizations, model improvements, new processes, and mostinformative data needs using new deep machine learning and Bayesian learning; and iv) Develop efficient computational methods for the ab"ove prediction, assimilation, and learning.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 30, 2019
- Source ID
- N000141912664
Entities
People
- Pierre Felix Lermusiaux
Organizations
- Massachusetts Institute of Technology
- Office of Naval Research
- United States Navy