Peetak P. Mitra

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David SchmidtUniversity of MassachusettsVerifierad e-postadress på acad.umass.edu
Gavin D PortwoodLawrence Livermore National LaboratoryVerifierad e-postadress på llnl.gov
Mateus Dias RibeiroStaff Scientist at DLRVerifierad e-postadress på dlr.de
Niccolò Dal SantoGoogleVerifierad e-postadress på google.com
Hansi SinghSchool of Earth and Ocean Sciences, University of Victoria, Victoria BC CanadaVerifierad e-postadress på uvic.ca
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerifierad e-postadress på caltech.edu
Michael (Misha) ChertkovChair, Program in Applied Mathematics, University of ArizonaVerifierad e-postadress på math.arizona.edu
Kalai RameaPalo Alto Research Center, Inc.Verifierad e-postadress på parc.com
Arvind T. MohanScientist, Computational Physics and Methods Group, Los Alamos National LaboratoryVerifierad e-postadress på lanl.gov
Venkatasubramanian ViswanathanAssociate Professor, University of MichiganVerifierad e-postadress på umich.edu
Andreas DengelProfessor of Computer Science, University of Kaiserslautern & Executive Director, DFKIVerifierad e-postadress på dfki.de
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice UniversityVerifierad e-postadress på rice.edu
Animesh GargGeorgia Institute of Technology, University of Toronto, NvidiaVerifierad e-postadress på gatech.edu
Tan NguyenUCLAVerifierad e-postadress på ucla.edu
Philip J RaschAffiliate Professor, University of WashingtonVerifierad e-postadress på u.washington.edu
Lyle M PickettSandia National LaboratoriesVerifierad e-postadress på sandia.gov
Christopher Vincent RackauckasMassachusetts Institute of TechnologyVerifierad e-postadress på mit.edu
Christopher F. PowellArgonne National LaboratoryVerifierad e-postadress på anl.gov
Peter Kelly SenecalOwner, Convergent Science Inc.Verifierad e-postadress på convergecfd.com
Vivek RamavajjalaGoogleVerifierad e-postadress på google.com

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Peetak P. Mitra

Founding Team/Scientist, Excarta

Verifierad e-postadress på excarta.io - Startsida

Scientific Machine Learning Weather Forecasting


Titel Sortera efter citat Sortera efter år Sortera efter titel	Citeras av Citeras av	År
Turbulence forecasting via neural ode GD Portwood, PP Mitra, MD Ribeiro, TM Nguyen, BT Nadiga, JA Saenz, ... arXiv preprint arXiv:1911.05180, 2019	57	2019
Acceleration of chemical kinetics computation with the learned intelligent tabulation (LIT) method M Haghshenas, P Mitra, ND Santo, DP Schmidt Energies 14 (23), 7851, 2021	11	2021
Learning non-linear spatio-temporal dynamics with convolutional Neural ODEs V Shankar, G Portwood, A Mohan, P Mitra, C Rackauckas, L Wilson, ... Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), 2020	10	2020
Identification and characterization of steady spray conditions in convergent, single-hole diesel injectors P Mitra, K Matusik, D Duke, P Srivastava, K Yasutomi, J Manin, L Pickett, ... SAE Technical Paper, 2019	7	2019
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow V Shankar, GD Portwood, AT Mohan, PP Mitra, D Krishnamurthy, ... Physics of Fluids 34 (11), 2022	5	2022
A data-driven approach to modeling turbulent decay at non-asymptotic Reynolds numbers MD Ribeiro, GD Portwood, P Mitra, TM Nyugen, BT Nadiga, M Chertkov, ... Bulletin of the American Physical Society, 2019	5	2019
The Eulerian Lagrangian Mixing-Oriented (ELMO) Model DP Schmidt, M Haghshenas, P Mitra, C Wang, PK Senecal, F Tagliante, ... International Journal of Multiphase Flow 152, 104041, 2022	4	2022
On the Effectiveness of Bayesian AutoML methods for Physics Emulators P Mitra, N Dal Santo, M Haghshenas, S Mitra, C Daly, DP Schmidt NeurIPS 2020 workshop on AI for Engineering Discovery, 2020	4	2020
A data-driven approach to modeling turbulent flows in an engine environment P Mitra, M Dias Ribeiro, D Schmidt APS Division of Fluid Dynamics Meeting Abstracts, G16. 003, 2019	4	2019
A comparison between CFD and 3D X-ray Diagnostics of Internal Flow in a CavitatingDiesel Injector Nozzle A Tekawade, P Mitra, BA Sforzo, KE Matusik, AL Kastengren, DP Schmidt, ... ILASS-Americas 30th Annual Conference on Liquid Atomization and Spray …, 2019	4	2019
CFD simulations of a two-phase ejector for transcritical CO2 cycles applied to supermarket refrigeration systems A Colombo, P Conti, M Orlandi, F Visconti, P Mitra, DP Schmidt SCIENCE ET TECHNIQUE DU FROID, 403-410, 2018	4	2018
Impact of Regional Marine Cloud Brightening Interventions on Climate Tipping Points H Hirasawa, D Hingmire, H Singh, P Rasch, P Mitra	3	2023
Rapid spatiotemporal turbulence modeling with convolutional Neural ODEs V Shankar, G Portwood, A Mohan, P Mitra, V Viswanathan, D Schmidt APS Division of Fluid Dynamics Meeting Abstracts, X11. 004, 2020	3	2020
Propagation of Surface-to-Low Earth Orbit Vortex Rings for Orbital Debris Management MA Noyes, P Mitra, A Dicholkar Safety is Not an Option, Proceedings of the 6th IAASS Conference 715, 11, 2013	3	2013
Improving CFD Simulations by Local Machine-Learned Corrections P Mitra, M Haghshenas, N Dal Santo, C Daly, DP Schmidt ASME International Mechanical Engineering Congress and Exposition 87660 …, 2023	2	2023
Verification against in-situ observations for Data-Driven Weather Prediction V Ramavajjala, PP Mitra arXiv preprint arXiv:2305.00048, 2023	2	2023
Machine Learning-based Anomaly Detection with Magnetic Data P Mitra, D Akhiyarov, M Araya-Polo, D Byrd NeurIPS 2020 workshop on AI for Engineering Discovery, 2020	2	2020
Towards building robust neural network models for fluid simulations P Mitra, M Haghshenas, N Dal Santo, C Daly, S Mitra, D Schmidt APS Division of Fluid Dynamics Meeting Abstracts, F09. 004, 2020	2	2020
LES Turbulence Model with Learnt Closure; Integration of DNN into a CFD Solver M Haghshenas, P Mitra, N Dal Santo, M Dias Ribeiro, S Mitra, D Schmidt APS Division of Fluid Dynamics Meeting Abstracts, S01. 019, 2020	2	2020
ClimFormer–A Spherical Transformer Model for Long-term Climate Projections SR Cachay, P Mitra, H Hirasawa, S Kim, S Hazarika, D Hingmire, P Rasch, ... NeurIPS workshop on Machine Learning for the Physical Sciences, 2022	1	2022

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