Statistical modeling of Ship’s hydrodynamic performance indicator

The traditional method used to estimate the hydrodynamic performance of a ship uses either the model test results or one of the many empirical methods to estimate and observe the trend in…

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

Most modeling approaches lie in either of the two categories: physics-based or data-driven. Recently, a third approach which is a combination of these deterministic and statistical models is emerging for…

Multifidelity computing for coupling full and reduced order models

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a…

PyDA: A Hands-On Introduction to Dynamical Data Assimilation with Python

Dynamic data assimilation offers a suite of algorithms that merge measurement data with numerical simulations to predict accurate state trajectories. Meteorological centers rely heavily on data assimilation to achieve trustworthy…

Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles

Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights into the…

Big Data Cybernetics

Norbert Wiener defined cybernetics in 1948 as “the scientific study of control and communication in the animal and the machine”. The objective of cybernetics is to steer a system towards…

HAM for next generation of digital twins

Highlight: The team of Suraj Pawar and Shady Ahmed wins the best paper award for this work at the Deep Wind 2021 COnference The physics-based modeling has been the workhorse…

Feature engineering and symbolic regression methods for detecting hidden physics from sparse sensors

In this study we put forth a modular approach for distilling hidden flow physics in discrete and sparse observations. To address functional expressiblity, a key limitation of the black-box machine…

Physics Guided Machine Learning

Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences. In this letter, we introduce a…

COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning

Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to…