Recent events

  • Our paper on using differentiable physics closure models for Burgers turbulence is accepted in Machine Learning Science and Technology. This work was led by Varun Shankar, a PhD candidate at Carnegie Mellon University. Read more here .
  • Alec's paper on building stable neural ordinary differential equations for chaotic dynamical systems is accepted in the Journal of Computational Physics! Congratulations to Alec and our co-authors. An arxiv preprint is available here.
  • Our work on deep-learning based surrogate modeling for internal combustion engine simulations is awarded the best paper in ASME Internal Combustion Engine Fall Conference, 2021. This was work in collaboration with Sudeepta Mondal, Gina Magnotti, Bethany Lusch, and Roberto Torelli.
  • Our large multi-institute collaborative project for Community Research on Climate and Urban Science (CROCUS) has been accepted! We will be working on developing novel parameterizations and surrogates for assessing the impact of tree canopies on street level simulations in collaboration with Drs. Kotamarthi, Fytanidis, and Fernando.
  • Romit will be presenting work related to learning and controlling dynamical systems at SIAM-CSE 2023, Amsterdam (Feb 26 - Mar 3).
  • Romit visited the SIAM Convening on Climate Science, Sustainability, and Clean Energy (DMS 2227218), Tysons Corner, Virginia, October 10-12, 2022. More details here.
  • Romit provided an invited talk at SIAM Mathematics of Data Science in San Diego on the 28th of September titled: "Stabilized Neural Ordinary Differential Equations for Learning Chaotic Dynamical Systems". See details here.
  • Romit visited the Department of Mathematics at the University of Pittsburgh on the 20th of September for a Computational Mathematics Seminar.
  • Dr. Shivam Barwey started as a postdoc in our group. Read more about his research here. Shivam will be working on graph-based scientific machine learning methods for surrogate modeling.
  • Romit provided an invited talk on ensemble-based uncertainty quantification for machine learning and was an early-career panelist at USACM UQ-MLIP. See slides here.
  • Romit provided a lecture on machine learning for dynamical systems at ATPESC 2022. See his talk slides here.
  • Romit provided an invited talk for surrogate modeling with neural ODEs at Virginia Tech for the ARIA conference.
  • Our joint work with the University of Texas at Dallas and the University of Utah for data analysis of wind-turbine wakes is 'Scilighted' and featured on the cover of the Journal of Sustainable and Renewable Energy! Read more here.
  • Romit delivered a tutorial on surrogate modeling for scientific applications at the University of Chicago Summer School on Data Science and AI. Code and slides are available on Github.
  • Romit (in collaboration with Bethany Lusch, Saumil Patel, Bulut Tekgul, and Dimitrios Fytanidis) organized a 4 hour workshop on coupling a Python-based data science ecosystem with OpenFOAM in the first ever PythonFOAM workshop! Get course materials including lecture videos and code here.
  • Romit has been awarded an "Impact Argonne Award" for "tackling several climate model challenges and advancing the field of downscaled climate modeling and impact analysis" by Argonne National Laboratory.
  • Our paper for surrogate modeling of multiphase closures using deep learning is featured by the Physics of Fluids.
  • Our novel deep learning architecture for time-varying unstructured data is highlighted by TechXplore.
  • Sahil Bhola is in the news! His work on exploring multifidelity deep reinforcement learning for aerodynamic optimization is highlighted by Argonne.
  • Our collaborative proposal to study the mathematics of surrogate modeling for nonlinear dynamical systems is accepted for funding by DOE.
  • Our collaborative proposal to study mathematical, computational, and hardware considerations for surrogate modeling of PDEs is accepted for funding by NSF.
  • Janah Richardson, an intern from Walter Payton College Prep High School, wins a gold medal for her ACT-SO project! She investigated structural inequalities using statistical modeling in Chicago. (see news story here).
  • Our research into using neural ordinary differential equations for reduced-order modeling is a Physica D. highly cited article! (scroll down the page to find our entry).
  • Our Physics of Fluids paper for surrogate modeling of advection-dominated flows using deep convolutional autoencoders is chosen as Editors' pick!
  • Our studies of simulation and data science interoperability have been selected for an Exascale Computing Project Proxy application.
  • NSF MSGI intern Dominic Skinner's excellent work is featured by DOE!
  • Our research on geophysical surrogate modeling using recurrent neural architecture search is covered by HPCWire, Newswise, and Insidehpc.
  • Our Physics of Fluids paper for stable non-intrusive reduced-order model using bidirectional LSTMs is chosen as Editors' pick!
  • Check out a feature story on my Margaret Butler Fellowship project.
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