Turbulence is of paramount importance in developing an informed understanding of a myriad of physical phenomena. IT is found in the fluids in our everyday lives to the fantastical plumes captured by…

This federal contract award, with an ID of 80NSSC22K1604, was granted to Rutgers, The State University, located in New Brunswick, New Jersey. The contract, valued at $150,000.00, is a grant for research to be conducted by Rutgers' Research Financial Services Division.

The research focuses on understanding and quantifying the role of fluid turbulence in various astrophysical environments, such as the interstellar medium, which is vital for understanding star formation and galaxy evolution. Rutgers plans to leverage advancements in machine learning and neural networks to develop new interpretable tools for turbulence modeling and pattern recognition. A portion of the research will also involve developing physics-informed convolutional neural networks for super-resolution on simulations of turbulent Rayleigh-Benard convection.

The contract was awarded by the Shared Services Center, a civilian federal agency, and does not have a set-aside designation. The award has an ultimate completion date of August 31, 2025.

Turbulence is of paramount importance in developing an informed understanding of a myriad of physical phenomena. IT is found in the fluids in our everyday lives to the fantastical plumes captured by the hubble telescope. However, the mechanics of turbulence are still a poorly understood, unsolved problem in classical physics.the core of my research is understanding and quantifying the keystone role that fluid turbulence plays in various astrophysical environments such as the interstellar medium (ISM). Understanding these processes is vital for attaining a holistic description of star formation, and ultimately galaxy evolution. Not only are such systems incredibly complex, with multiple competing factors contributing to the phenomena observed, given the unfathomable distances that these objects are away from US, turbulence is especially difficult to quantify observationally in extraterrestrial systems. IT is difficult, if not impossible, to attain neat analytic.solutions to such chaotic systems involving multiple spatial and temporal scales. However, following an incredible decade in the rapid development of automation tools, machine learning (ML) techniques and neural networks (NN) are the state of the art when IT comes to pattern recognition technology, and i will leverage these advances to bring about symbiotic development of both realms of astronomy and ML.my approach is to develop new interpretable ML tools for turbulence that simultaneously address challenges both in fluid mechanics and deep learning (DL) practices. A grand challenge in the study of fluid turbulence is to achieve detailed numerical simulations. To address this, a portion of this research focuses on developing physics-informed convolutional neural networks for super-resolution on simulations of turbulent rayleigh-benard convection (RBC).