Sandeep Reddy B

Sandeep Reddy B

Research Scientist

National Energy Technology Laboratory (NETL)

Biography

I am a research scientist at Leidos Research Support Team (LRST) which is involved in executing a mulit-year and multi-million dollar research service support (RSS) contract with Department of Energy (DOE) at National Energy Technology Laboratory (NETL), pittsburgh. My work is primarily focused towards developing efficient methods for intelligent monitoring of natural gas pipelines at NETL as part of its multi-disciplinary research project, Natural Gas Infrastucture Program.

In my work as a postdoctoral research scientist at Max Planck Institute, Germany i was actively involved in developing innovative methods for discovery of parsimonious nonlinear dynamical models using the concepts from scientific machine learning. I was also part of Max Planck research network on big data-driven material science BiGmax. Prior to MPI, I worked as a research scientist at TCOMS under Prof Chan Eng Soon and developed fast and accurate methods for reconstruction and propagation of multidirectional ocean wave fields. My work at TCOMS broadly touched upon concepts ranging from compressed sensing, sparse representation, model order reduction, proper orthogonal decomposition, physics informed A.I and custom made ML networks such as Fourier neural operators. In my doctoral work under Prof A R Magee and Prof R K Jaiman, I focused on developing data-driven methods for stability analysis and prediction of fluid-structure interaction. As part of my PhD research, I pursued topics ranging from system identification, ERA, Recurrent neural networks, Convolutional neural networks, Proper orthogonal decompisition and data-driven reduced order models.

I am always open for any kind of discussion on anything related to my work or any interesting ideas or topics.

In addition to the above, i try to do… 🏃 🥋 🏎️ 🏏 🎦 in my free time.

Interests
  • Machine learning/ Deep learning
  • Physics-informed A.I.
  • Model order reduction
  • Nonlinear dynamics
  • Applied mathematics
  • Computational physics
Education
  • PhD in Data-driven computational fluid mechanics, 2020

    National University of Singapore

  • M.tech in Applied mechanics, 2015

    Indian Institute of Technology, Madras

  • B.tech in Naval architecture and Ocean engineering, 2014

    Indian Institute of Technology, Madras

Experience

 
 
 
 
 
National Energy Technology Laboratory (NETL)
Research Scientist
Jan 2023 – Present Pittsburgh, USA

Intelligent sensing and monitoring of natural gas piplelines

  • Supporting Leidos in executing a mulit-year and multi-million dollar research service support (RSS) contract with Department of Energy (DOE) at National Energy Technology Laboratory (NETL),pittsburgh
  • This project carries a lot of national significance because it directly enables DOE to address the Nation’s energy challenges via innovative technological solutions, which will be developed at NETL as part of this contract
  • Part of a big collaborative team with people from diverse backgrounds and experiences.
  • Technical lead and PoC for the development of digital twin of natural gas pipeline and directly responsible or all the efforts on combining A.I. with numerical simulations.
 
 
 
 
 
Max Planck Institute for Dynamics of Complex Technical Systems
Postdoctoral Research Scientist
Sep 2021 – Sep 2022 Magdeburg, Germany

Physics Enhanced Machine Learning

  • Developmemt of parsimonious dynamical models using scientific machine learning.
  • Discovery of physically interpretable continuum models of materials science from experimental data
 
 
 
 
 
TCOMS
Scientist
Mar 2020 – Aug 2021 Singapore

Development of digital twin of ocean wave environment

  • Reconstruction of ocean wave field from instantaneous probe data using the concepts of compressed sensing
  • Reduced order models for fast propagation of multi-directional ocean wave fields
  • Data-driven models for reconstruction and propagation of multi- directional ocean wave fields
 
 
 
 
 
National University of Singapore
Research Engineer
Sep 2019 – Mar 2020 Singapore
Model order reduction for nonlinear evolution of ocean waves
 
 
 
 
 
National University of Singapore
Doctoral scholar
Aug 2015 – Aug 2019 Singapore

Data-driven computing for stability analysis and prediction of fluid-structure interaction

  • Data-driven computing for stability analysis of passive suppression
  • Hybrid reduced order model for fluid structure interaction
  • Convolutional recurrent autoencoder networks for complete predic- tion of flow field

Accomplish­ments

Coursera
Deep learning
See certificate
Coursera
Tensorflow developer
See certificate

News

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