For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr-Sommerfeld stability equation (1907) and…
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…
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…
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…