Peetak Mitra

I am a fifth (and final) year Ph.D. candidate at the University of Massachusetts, Amherst. I work in the area of Machine Learning and predictive fluid modeling and I am advised by David Paul Schmidt. I completed part of my dissertation research at the Los Alamos National Laboratory, where I worked on developing machine learning frameworks to model geophysical turbulence processes, a critical driver of energy exchange in the oceans and atmosphere. I spent the summer of 2020 working (remotely) with TOTAL S.A. R&D, in developing High Performance Machine Learning codes using TensorFlow and PyTorch.

The other major thrust of my research is within the ICEnet Consortium, an industry-funded data-driven consortium that I co-founded with my doctoral advisor Prof. David P. Schmidt. ICEnet leverages the power of Artificial Intelligence (AI) to develop predictive machine learning algorithms to improve engine design, helping create fuel-efficient engines capable of meeting stricter environmental regulations. Among the partners for ICEnet include leading engine CFD software makers/users such as SIEMENS-CD/Adapco, Cummins, Convergent Science and AVL as well as NVIDIA and MathWorks.

For my early Ph.D. research I worked on developing reduced order models (ROM) for advanced propulsion systems, working in close collaboration with U.S. Department of Energy national laboratories - Argonne National Laboratory and Sandia National Laboratories, in a $2.1 million industry funded project, Spray Combustion Consortium. Models that I have developed have been implemented within commercial software code Converge CFD, and is in use by major automotive engine makers from around the world.

Apart from my research, I am a part of the core-team at Climate Change AI, a volunteer network that catalyzes impactful work at the intersection of machine learning and climate change and lead the monthly newsletter. I have regularly served as a reviewer for prestigious machine learning journals and conferences such as Nat. Machine Intelligence, and NeurIPS, among others.

Recent News:

  • Upcoming invited talk at 16th U.S. National Congress on Computational Mechanics, 7/21. Link: TBP

  • Talk on using Machine Learning for fluids accepted at NVIDIA-GTC 2021, will be available 4/21. Link: TBP

  • Presented talk at SIAM CSE 2021: Learning Operators from Data Symposium 2021, Dallas, TX, 'Non-Intrusive Machine Learning Models for Fluid Simulations'

  • Paper accepted to SAE WCX 2021: 'Analysis and Interpretation of Data driven closure models relevant to Internal Combustion Engines'

  • Presented three papers at Neural Informational Processing Systems workshops

    • Paper 1: Learning non-linear spatio-temporal dynamics with Convolutional Neural ODEs

    • Paper 2: Machine Learning based Anomaly Detection with Magnetic Data

    • Paper 3: On the Effectiveness of Bayesian autoML methods for physics emulators

  • Blog featured by Towards Data Science on Medium [Link]

  • Blog published by MathWorks [Link]

  • Blog published by Venkat Viswanathan [Carnegie Mellon University] [Link]

  • Presented three talks at the 73rd meeting of the American Physical Society's Division of Fluid Dynamics 2020 meeting

Community service

As part of my community service, I advise undergraduate and early graduate degree (first year Masters) students. Additionally I regularly review papers from prestigious scientific journals and machine learning conferences, including:

  • Nature Machine Intelligence Journal

  • Neural Networks Journal

  • Atomization and Sprays Journal

  • Neural Information Processing Systems (NeurIPS) Workshops on:

    • Machine Learning for the Physical Sciences 2020,

    • Algorithmic Fairness through the lens of causality and Interpretability, 2020

  • Tackling Climate Change with Machine Learning workshop at the International Conference on Learning Representations (ICLR) 2020 and a meta-reviewer for the International Conference on Machine Learning (ICML) 2021


Key Publications

Papers and Presentations :

Upcoming:

  • Haghshenas, M., Mitra, P., Wang, C., Tagliante, F., Senecal, P.K., Pickett, L.P., Schmidt, D.P., Improved Methods for Mixing-Limited Spray Modeling, submitted to ILASS 2021

  • Mitra, P.P., Dal Santo, N., Haghshenas, M., Mitra, S.P., Schmidt D.P., Pruning strategies for Scientific data, submitted to ICLR 2021 Workshop

  • Mitra, P.P., Schmidt D.P., Characterizing cavitation within single-hole cylindrical and multi-hole convergent nozzles, to be submitted to Atomization and Sprays Journal

Presented (including submitted) :

  • Schmidt D.P., Mitra, P. P., Haghshenas, M., Wang, C., Tagliante, F., Senecal, P.K, Pickett, L.M., Eulerian Lagrangian Mixing Oriented (ELMO) Model, submitted to International Journal of Multiphase Flow

  • Mitra, P.P., Haghshenas, M., Dal Santo, N., Mitra, S.P., Schmidt, D.P., Machine Learning for Internal Combustion Engine Simulations, submitted to SAE WCX 2021

  • Haghshenas, M., Mitra, P.P., Dal Santo, N., Dias Ribeiro, M., Mitra, S.P., Schmidt, D.P., LES Turbulence Model with Learnt Closure; Integration of DNN into a CFD Solver, to feature at American Physical Society DFD 2020

  • Mitra P.P., Haghshenas, M., Dal Santo, N., Daly, C., Mitra, S.P., Schmidt, D.P., Towards Building Robust Neural Network Models for Fluid Simulations, to feature at American Physical Society DFD 2020

  • Mitra, P.P., Haghshenas, M., Schmidt, D.P., Super Resolution approach for accelerating fluid solvers, to feature at American Physical Society DFD 2020

  • Shankar, V., Portwood, G.D., Mohan, A.T., Mitra, P.P., Viswanathan, V., Schmidt, D.P., Rapid Spatiotemporal Turbulence Modeling with Convolutional Neural ODEs, to feature at American Physical Society DFD 2020

  • Tekawade, A., P. Mitra, B. A. Sforzo, K. E. Matusik, A. L. Kastengren, D. P. Schmidt, and C. F. Powell. "A comparison between CFD and 3D X-ray Diagnostics of Internal Flow in a Cavitating Diesel Injector Nozzle." In ILASS-Americas 30th Annual Conference on Liquid Atomization and Spray Systems, Tempe, AZ. 2019.

  • Ribeiro, Mateus Dias, Gavin D. Portwood, Peetak Mitra, Tan Mihn Nyugen, Balasubramanya T. Nadiga, Michael Chertkov, Anima Anandkumar, and David P. Schmidt. "A data-driven approach to modeling turbulent decay at non-asymptotic reynolds numbers." Bulletin of the American Physical Society (2019).

  • Portwood, Gavin D., Peetak P. Mitra, Mateus Dias Ribeiro, Tan Minh Nguyen, Balasubramanya T. Nadiga, Juan A. Saenz, Michael Chertkov et al. "Turbulence forecasting via Neural ODE." arXiv:1911.05180 (2019).

  • Mitra, Peetak, Mateus Dias Ribeiro, and David Schmidt. "A data-driven approach to modeling turbulent flows in an engine environment." APS (2019): G16-003.

  • Mitra, Peetak, and Suhrid Deshmukh. "Modeling PKT at a global level: A machine learning approach." arXiv preprint arXiv:1908.00624 (2019).

  • Mitra, Peetak, Katarzyna Matusik, Daniel Duke, Priyesh Srivastava, Koji Yasutomi, Julien Manin, Lyle Pickett et al. Identification and characterization of steady spray conditions in convergent, single-hole diesel injectors. No. 2019-01-0281. SAE Technical Paper, 2019.

  • Mitra, Peetak. "Pedestrian Collision Avoidance System (PeCAS): a Deep Learning Framework." arXiv preprint arXiv:1811.04453 (2018).

  • Colombo, Alessandro, Pierangelo Conti, Maurizio Orlandi, Federico Visconti, Peetak Mitra, and David P. Schmidt. "CFD simulations of a two-phase ejector for transcritical CO2 cycles applied to supermarket refrigeration systems." In 13th IIR Gustav Lorentzen Conference on Natural Refrigerants: Natural Refrigerant Solutions for Warm Climate Countries, pp. 403-410. International Institute of Refrigeration, 2018.

  • Noyes, Matthew A., Peetak Mitra, and Antariksh Dicholkar. "Propagation of Surface-to-Low Earth Orbit Vortex Rings for Orbital Debris Management." ESASP 715 (2013): 11.

  • Noyes, Matthew A., Rugaber, G., Mitra, P.P., Dicholkar, A., "Propagation of Surface-to-LEO Vortex Rings For Orbital Debris Management." In 5th AIAA Atmospheric and Space Environments Conference, p. 2682. 2013.


Key Invited Talks (including upcoming):

  • SIAM - Learning Operators from Data Symposium 2021, Dallas, TX, 'Non-Intrusive Machine Learning Models for Fluid Simulations'

  • Parallel CFD International Conference 2020, Nice, France, 'Neural ODEs for Reduced Order Models' - (to be delivered by collaborator, Dr. M. Dias Ribeiro)

  • AISC Spotlight Talk 2020, 'ML in Climate', [LINK]

  • GPU Technology Conference, 2020, Santa Clara, CA: 'Climate sub-closures using GPUs' [LINK]

  • New York Academy of Sciences - Machine Learning Symposium 2020, New York, New York, 'Neural ODEs for Reduced Order Models'

  • Physics Informed Machine Learning, 2020, Santa Fe, NM: 'Turbulence Forecasting via Neural ODEs'


Prominent Awards:

  • Google Cloud Platform computing award, 2019. Grant value: $5000

  • National Science Foundation, Research proposal award 2019, Grant value: $125,000

  • NeurIPS Travel award 2019, Award value: $1500 + Complimentary registration

  • Selected to be a citizen-scientist astronaut, Project PoSSUM, class of 2016

  • Member of the month, UN-Space Generation Advisory Council, 2013

  • Best Tracking System award, NASA Robotics Mining Competition, 2011

Select Invited Talk Recordings

GTC 2020

AISC 2020

Climate Change AI ICLR 2020 workshop

Research Gallery

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Contact

212 Marston Hall, Department of Mechanical Engineering, University of Massachusetts, Amherst, MA 01003

E-mail: pmitra [@] umass [dot] edu

Connect with me via LinkedIn or Twitter!