Experiences in driving a cave with IBM scalable graphics engine-3 (SGE-3) prototypes. - Prabhat; Samuel G. Fulcomer (VRST, 2005)

Adviser: Immersive Field Work for Planetary Geoscientists. - Andrew S. Forsberg; Prabhat; Graff Haley; Andrew Bragdon; Joseph Levy; Caleb I. Fassett; David Shean; James W. Head III; Sarah Milkovich; Mark A. Duchaineau (IEEE Computer Graphics and Applications, 2006)

Automated Analysis for Detecting Beams in Laser Wakefield Simulations. - Daniela Mayumi Ushizima; Oliver Rübel; Prabhat; Gunther H. Weber; E. Wes Bethel; Cecilia R. Aragon; Cameron G. R. Geddes; Estelle Cormier-Michel; Bernd Hamann; Peter Messmer; Hans Hagen (ICMLA, 2008)

High performance multivariate visual data exploration for extremely large data. - Oliver Rübel; Prabhat; Kesheng Wu; Hank Childs; Jeremy S. Meredith; Cameron G. R. Geddes; Estelle Cormier-Michel; Sean Ahern; Gunther H. Weber; Peter Messmer; Hans Hagen; Bernd Hamann; et al. (SC, 2008)

A Comparative Study of Desktop, Fishtank, and Cave Systems for the Exploration of Volume Rendered Confocal Data Sets. - Prabhat; Andrew S. Forsberg; Michael Katzourin; Kristi Wharton; Mel Slater (IEEE Trans. Vis. Comput. Graph., 2008)

Extreme Scaling of Production Visualization Software on Diverse Architectures. - Hank Childs; David Pugmire; Sean Ahern; Brad Whitlock; Mark Howison; Prabhat; Gunther H. Weber; E. Wes Bethel (IEEE Computer Graphics and Applications, 2010)

Coupling visualization and data analysis for knowledge discovery from multi-dimensional scientific data. - Oliver Rübel; Sean Ahern; E. Wes Bethel; Mark D. Biggin; Hank Childs; Estelle Cormier-Michel; Angela H. DePace; Michael B. Eisen; Charless C. Fowlkes; Cameron G. R. Geddes; Hans Hagen; Bernd Hamann; et al. (ICCS, 2010)

FastQuery: A Parallel Indexing System for Scientific Data. - Jerry Chi-Yuan Chou; Kesheng Wu; Prabhat (CLUSTER, 2011)

Parallel Kriging Analysis for Large Spatial Datasets. - Wei Zhuo 001; Prabhat; Christopher J. Paciorek; Cari Kaufman; Wes Bethel (ICDM Workshops, 2011)

Parallel index and query for large scale data analysis. - Jerry Chi-Yuan Chou; Mark Howison; Brian Austin; Kesheng Wu; Ji Qiang; E. Wes Bethel; Arie Shoshani; Oliver Rübel; Prabhat; Robert D. Ryne (SC, 2011)

FastQuery: A General Indexing and Querying System for Scientific Data. - Jerry Chi-Yuan Chou; Kesheng Wu; Prabhat (SSDBM, 2011)

Experiences with 100Gbps network applications. - Mehmet Balman; Eric Pouyoul; Yushu Yao; E. Wes Bethel; Burlen Loring; Prabhat; John Shalf; Alex Sim; Brian L. Tierney (DICT@HPDC, 2012)

Abstract: Auto-Tuning of Parallel IO Parameters for HDF5 Applications. - Babak Behzad; Joey Huchette; Huong Luu 002; Ruth A. Aydt; Quincey Koziol; Prabhat; Surendra Byna; Mohamad Chaarawi; Yushu Yao (SC Companion, 2012)

Parallel I/O, analysis, and visualization of a trillion particle simulation. - Surendra Byna; Jerry Chi-Yuan Chou; Oliver Rübel; Prabhat; Homa Karimabadi; William S. Daughton; Vadim Roytershteyn; E. Wes Bethel; Mark Howison; Ke-Jou Hsu; Kuan-Wu Lin; Arie Shoshani; et al. (SC, 2012)

A System for Query Based Analysis and Visualization. - Allen R. Sanderson; Brad Whitlock; Oliver Rübel; Hank Childs; Gunther H. Weber; Prabhat; Kenseng Wu (EuroVA@EuroVis, 2012)

Accelerating analysis of void space in porous materials on multicore and GPU platforms. - Richard L. Martin 001; Prabhat; David Donofrio; James A. Sethian; Maciej Haranczyk (Int. J. High Perform. Comput. Appl., 2012)

TECA: A Parallel Toolkit for Extreme Climate Analysis. - Prabhat; Oliver Rübel; Surendra Byna; Kesheng Wu; Fuyu Li; Michael F. Wehner; Wes Bethel (ICCS, 2012)

A framework for auto-tuning HDF5 applications. - Babak Behzad; Joey Huchette; Huong Vu Thanh Luu; Ruth A. Aydt; Surendra Byna; Yushu Yao; Quincey Koziol; Prabhat (HPDC, 2013)

Taming parallel I/O complexity with auto-tuning. - Babak Behzad; Huong Vu Thanh Luu; Joseph Huchette; Surendra Byna; Prabhat; Ruth A. Aydt; Quincey Koziol; Marc Snir (SC, 2013)

Why high performance visual data analytics is both relevant and difficult. - E. Wes Bethel; Prabhat; Surendra Byna; Oliver Rübel; K. John Wu; Michael F. Wehner (Visualization and Data Analysis, 2013)

Ultrascale Visualization of Climate Data. - Dean N. Williams; Peer-Timo Bremer; Charles M. Doutriaux; John Patchett; Sean Williams; Galen M. Shipman; Ross Miller; David Pugmire; Brian E. Smith; Chad A. Steed; E. Wes Bethel; Hank Childs; et al. (Computer, 2013)

Parallelizing Gaussian Process Calculations in R - Christopher J. Paciorek; Benjamin Lipshitz; Wei Zhuo 001; Prabhat; Cari Kaufman; Rollin C. Thomas (CoRR, 2013)

A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data. - Wei-Chen Chen; George Ostrouchov; David Pugmire; Prabhat; Michael F. Wehner (Technometrics, 2013)

Improving parallel I/O autotuning with performance modeling. - Babak Behzad; Surendra Byna; Stefan M. Wild; Prabhat; Marc Snir (HPDC, 2014)

TECA: Petascale Pattern Recognition for Climate Science. - Prabhat; Surendra Byna; Venkatram Vishwanath; Eli Dart; Michael F. Wehner; William D. Collins (CAIP (2), 2015)

Dynamic Model-Driven Parallel I/O Performance Tuning. - Babak Behzad; Surendra Byna; Stefan M. Wild; Prabhat; Marc Snir (CLUSTER, 2015)

A Multiplatform Study of I/O Behavior on Petascale Supercomputers. - Huong Luu 002; Marianne Winslett; William Gropp; Robert B. Ross; Philip H. Carns; Kevin Harms; Prabhat; Surendra Byna; Yushu Yao (HPDC, 2015)

Celeste: Variational inference for a generative model of astronomical images. - Jeffrey Regier; Andrew C. Miller; Jon McAuliffe; Ryan P. Adams; Matthew D. Hoffman; Dustin Lang; David Schlegel; Prabhat (ICML, 2015)

Scalable Bayesian Optimization Using Deep Neural Networks. - Jasper Snoek; Oren Rippel; Kevin Swersky; Ryan Kiros; Nadathur Satish; Narayanan Sundaram; Md. Mostofa Ali Patwary; Prabhat; Ryan P. Adams (ICML, 2015)

A Gaussian Process Model of Quasar Spectral Energy Distributions. - Andrew C. Miller; Albert Wu; Jeffrey Regier; Jon McAuliffe; Dustin Lang; Prabhat; David Schlegel; Ryan P. Adams (NIPS, 2015)

Pattern-driven parallel I/O tuning. - Babak Behzad; Surendra Byna; Prabhat; Marc Snir (PDSW@SC, 2015)

BD-CATS: big data clustering at trillion particle scale. - Md. Mostofa Ali Patwary; Surendra Byna; Nadathur Rajagopalan Satish; Narayanan Sundaram; Zarija Lukic; Vadim Roytershteyn; Michael J. Anderson; Yushu Yao; Prabhat; Pradeep Dubey (SC, 2015)

Techniques for modeling large-scale HPC I/O workloads. - Shane Snyder; Philip H. Carns; Robert Latham; Misbah Mubarak; Robert B. Ross; Christopher D. Carothers; Babak Behzad; Huong Vu Thanh Luu; Surendra Byna; Prabhat (PMBS@SC, 2015)

Matrix factorizations at scale: A comparison of scientific data analytics in spark and C+MPI using three case studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (IEEE BigData, 2016)

Data Elevator: Low-Contention Data Movement in Hierarchical Storage System. - Bin Dong 002; Suren Byna; Kesheng Wu; Prabhat; Hans Johansen; Jeffrey N. Johnson; Noel Keen (HiPC, 2016)

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks. - Evan Racah; Seyoon Ko; Peter J. Sadowski; Wahid Bhimji; Craig Tull; Sang-Yun Oh; Pierre Baldi; Prabhat (ICMLA, 2016)

A Multi-Platform Evaluation of the Randomized CX Low-Rank Matrix Factorization in Spark. - Alex Gittens; Jey Kottalam; Jiyan Yang; Michael F. Ringenburg; Jatin Chhugani; Evan Racah; Mohitdeep Singh; Yushu Yao; Curt Fischer; Oliver Rübel; Benjamin P. Bowen; Norman G. Lewis; et al. (IPDPS Workshops, 2016)

PANDA: Extreme Scale Parallel K-Nearest Neighbor on Distributed Architectures. - Md. Mostofa Ali Patwary; Nadathur Rajagopalan Satish; Narayanan Sundaram; Jialin Liu 002; Peter J. Sadowski; Evan Racah; Surendra Byna; Craig Tull; Wahid Bhimji; Prabhat; Pradeep Dubey (IPDPS, 2016)

Matrix Factorization at Scale: a Comparison of Scientific Data Analytics in Spark and C+MPI Using Three Case Studies. - Alex Gittens; Aditya Devarakonda; Evan Racah; Michael F. Ringenburg; Lisa Gerhardt; Jey Kottalam; Jialin Liu 002; Kristyn J. Maschhoff; Shane Canon; Jatin Chhugani; Pramod Sharma; Jiyan Yang; et al. (CoRR, 2016)

Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. - Yunjie Liu; Evan Racah; Prabhat; Joaquin Correa; Amir Khosrowshahi; David Lavers; Kenneth Kunkel; Michael F. Wehner; William D. Collins (CoRR, 2016)

Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. - Evan Racah; Christopher Beckham; Tegan Maharaj; Prabhat; Christopher J. Pal (CoRR, 2016)

Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference. - Jeffrey Regier; Kiran Pamnany; Ryan Giordano; Rollin C. Thomas; David Schlegel; Jon McAuliffe; Prabhat (CoRR, 2016)

Methods for Specifying Scientific Data Standards and Modeling Relationships with Applications to Neuroscience. - Oliver Rübel; Max Dougherty; Prabhat; Peter Denes; David Conant; Edward F. Chang; Kristofer E. Bouchard (Frontiers Neuroinformatics, 2016)

Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction. - Kristofer E. Bouchard; Alejandro F. Bujan; Farbod Roosta-Khorasani; Shashanka Ubaru; Prabhat; Antoine Snijders; Jian-Hua Mao; Edward F. Chang; Michael W. Mahoney; Sharmodeep Bhattacharyya (NIPS, 2017)

ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. - Evan Racah; Christopher Beckham; Tegan Maharaj; Samira Ebrahimi Kahou; Prabhat; Chris Pal (NIPS, 2017)

Performance analysis of emerging data analytics and HPC workloads. - Christopher S. Daley; Prabhat; Sudip S. Dosanjh; Nicholas J. Wright (PDSW-DISCS@SC, 2017)

Galactos: computing the anisotropic 3-point correlation function for 2 billion galaxies. - Brian Friesen; Md. Mostofa Ali Patwary; Brian Austin; Nadathur Satish; Zachary Slepian; Narayanan Sundaram; Deborah Bard; Daniel J. Eisenstein; Jack Deslippe; Pradeep Dubey; Prabhat (SC, 2017)

Deep learning at 15PF: supervised and semi-supervised classification for scientific data. - Thorsten Kurth; Jian Zhang 049; Nadathur Satish; Evan Racah; Ioannis Mitliagkas; Md. Mostofa Ali Patwary; Tareq M. Malas; Narayanan Sundaram; Wahid Bhimji; Mikhail Smorkalov; Jack Deslippe; Mikhail Shiryaev; et al. (SC, 2017)

ArrayBridge: Interweaving declarative array processing with high-performance computing. - Haoyuan Xing; Sofoklis Floratos; Spyros Blanas; Suren Byna; Prabhat; Kesheng Wu; Paul Brown (CoRR, 2017)

An Assessment of Data Transfer Performance for Large-Scale Climate Data Analysis and Recommendations for the Data Infrastructure for CMIP6. - Eli Dart; Michael F. Wehner; Prabhat (CoRR, 2017)

Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC. - Wahid Bhimji; Steven Andrew Farrell; Thorsten Kurth; Michela Paganini; Prabhat; Evan Racah (CoRR, 2017)

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators. - Mario Lezcano Casado; Atilim Gunes Baydin; David Martínez-Rubio; Tuan Anh Le 001; Frank D. Wood; Lukas Heinrich; Gilles Louppe; Kyle Cranmer; Karen Ng; Wahid Bhimji; Prabhat (CoRR, 2017)

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer. - Amrita Mathuriya; Thorsten Kurth; Vivek Rane; Mustafa Mustafa; Lei Shao; Debbie Bard; Prabhat; Victor W. Lee (CoRR, 2017)

ArrayBridge: Interweaving Declarative Array Processing in SciDB with Imperative HDF5-Based Programs. - Haoyuan Xing; Sofoklis Floratos; Spyros Blanas; Suren Byna; Prabhat; Kesheng Wu; Paul Brown (ICDE, 2018)

Graph Neural Networks for IceCube Signal Classification. - Nicholas Choma; Federico Monti; Lisa Gerhardt; Tomasz Palczewski; Zahra Ronaghi; Prabhat; Wahid Bhimji; Michael M. Bronstein; Spencer R. Klein; Joan Bruna (ICMLA, 2018)

Cataloging the Visible Universe Through Bayesian Inference at Petascale. - Jeffrey Regier; Kiran Pamnany; Keno Fischer; Andreas Noack 001; Maximilian Lam; Jarrett Revels; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (IPDPS, 2018)

Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries using Alchemist. - Alex Gittens; Kai Rothauge; Shusen Wang; Michael W. Mahoney; Lisa Gerhardt; Prabhat; Jey Kottalam; Michael F. Ringenburg; Kristyn J. Maschhoff (KDD, 2018)

Evaluation of HPC Application I/O on Object Storage Systems. - Jialin Liu 002; Quincey Koziol; Gregory F. Butler; Neil Fortner; Mohamad Chaarawi; Houjun Tang; Suren Byna; Glenn K. Lockwood; Ravi Cheema; Kristy A. Kallback-Rose; Damian Hazen; Prabhat (PDSW-DISCS@SC, 2018)

Exascale deep learning for climate analytics. - Thorsten Kurth; Sean Treichler; Joshua Romero; Mayur Mudigonda; Nathan Luehr; Everett H. Phillips; Ankur Mahesh; Michael A. Matheson; Jack Deslippe; Massimiliano Fatica; Prabhat; Michael Houston (SC, 2018)

CosmoFlow: using deep learning to learn the universe at scale. - Amrita Mathuriya; Deborah Bard; Peter Mendygral; Lawrence Meadows; James Arnemann; Lei Shao; Siyu He; Tuomas Kärnä; Diana Moise; Simon J. Pennycook; Kristyn J. Maschhoff; Jason Sewall; et al. (SC, 2018)

Interactive Distributed Deep Learning with Jupyter Notebooks. - Steven Andrew Farrell; Aaron Vose; Oliver Evans; Matthew L. Henderson; Shreyas Cholia; Fernando Pérez; Wahid Bhimji; Shane Canon; Rollin C. Thomas; Prabhat (ISC Workshops, 2018)

International Neuroscience Initiatives through the Lens of High-Performance Computing. - Kristofer E. Bouchard; James B. Aimone; Miyoung Chun; Thomas Dean; Michael Denker; Markus Diesmann; David Donofrio; Loren M. Frank; Narayanan Kasthuri; Christof Koch; Oliver Rübel; Horst D. Simon; et al. (Computer, 2018)

Approximate Inference for Constructing Astronomical Catalogs from Images. - Jeffrey Regier; Andrew C. Miller; David Schlegel; Ryan P. Adams; Jon D. McAuliffe; Prabhat (CoRR, 2018)

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin; Lukas Heinrich; Wahid Bhimji; Bradley Gram-Hansen; Gilles Louppe; Lei Shao; Prabhat; Kyle Cranmer; Frank D. Wood (CoRR, 2018)

Optimizing the Union of Intersections LASSO (UoILASSO) and Vector Autoregressive (UoIVAR) Algorithms for Improved Statistical Estimation at Scale. - Mahesh Balasubramanian 001; Trevor D. Ruiz; Brandon Cook 001; Sharmodeep Bhattacharyya; Prabhat; Aviral Shrivastava; Kristofer E. Bouchard (CoRR, 2018)

Learning to Focus and Track Extreme Climate Events. - Sookyung Kim; Sunghyun Park 005; Sunghyo Chung; Joonseok Lee; Yunsung Lee; Hyojin Kim 001; Prabhat; Jaegul Choo (BMVC, 2019)

Spherical CNNs on Unstructured Grids. - Chiyu Max Jiang; Jingwei Huang 001; Karthik Kashinath; Prabhat; Philip Marcus; Matthias Nießner (ICLR (Poster), 2019)

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. - Atilim Gunes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Saeid Naderiparizi; Andreas Munk 001; Jialin Liu 002; Bradley Gram-Hansen; Gilles Louppe; Lawrence Meadows; Philip H. S. Torr; Victor W. Lee; et al. (NeurIPS, 2019)

Etalumis: bringing probabilistic programming to scientific simulators at scale. - Atilim Günes Baydin; Lei Shao; Wahid Bhimji; Lukas Heinrich; Lawrence Meadows; Jialin Liu 002; Andreas Munk 001; Saeid Naderiparizi; Bradley Gram-Hansen; Gilles Louppe; Mingfei Ma; Xiaohui Zhao; et al. (SC, 2019)

DisCo: Physics-Based Unsupervised Discovery of Coherent Structures in Spatiotemporal Systems. - Adam Rupe; Prabhat; James P. Crutchfield; Nalini Kumar; Vladislav Epifanov; Karthik Kashinath; Oleksandr Pavlyk; Frank Schlimbach; Mostofa Patwary; Sergey Maidanov; Victor W. Lee (MLHPC@SC, 2019)

Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. - Liu Yang; Prabhat; George E. Karniadakis; Sean Treichler; Thorsten Kurth; Keno Fischer; David A. Barajas-Solano; Joshua Romero; Valentin Churavy; Alexandre M. Tartakovsky; Michael Houston (DLS@SC, 2019)

Acceleration of Scientific Deep Learning Models on Heterogeneous Computing Platform with Intel® FPGAs. - Chao Jiang; Dave Ojika; Thorsten Kurth; Prabhat; Sofia Vallecorsa; Bhavesh Patel; Herman Lam (ISC Workshops, 2019)

Deep-Hurricane-Tracker: Tracking and Forecasting Extreme Climate Events. - Sookyung Kim; Hyojin Kim 001; Joonseok Lee; Sangwoong Yoon; Samira Ebrahimi Kahou; Karthik Kashinath; Prabhat (WACV, 2019)

Alchemist: An Apache Spark ⇔ MPI interface. - Alex Gittens; Kai Rothauge; Shusen Wang; Michael W. Mahoney; Jey Kottalam; Lisa Gerhardt; Prabhat; Michael F. Ringenburg; Kristyn J. Maschhoff (Concurr. Comput. Pract. Exp., 2019)

Towards Unsupervised Segmentation of Extreme Weather Events. - Adam Rupe; Karthik Kashinath; Nalini Kumar; Victor W. Lee; Prabhat; James P. Crutchfield (CoRR, 2019)

Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs. - Liu Yang; Sean Treichler; Thorsten Kurth; Keno Fischer; David A. Barajas-Solano; Joshua Romero; Valentin Churavy; Alexandre M. Tartakovsky; Michael Houston; Prabhat; George E. Karniadakis (CoRR, 2019)

Cataloging the visible universe through Bayesian inference in Julia at petascale. - Jeffrey Regier; Keno Fischer; Kiran Pamnany; Andreas Noack 001; Jarrett Revels; Maximilian Lam; Steve Howard; Ryan Giordano; David Schlegel; Jon McAuliffe; Rollin C. Thomas; Prabhat (J. Parallel Distributed Comput., 2019)

Deep learning and process understanding for data-driven Earth system science. - Markus Reichstein; Gustau Camps-Valls; Bjorn Stevens; Martin Jung 002; Joachim Denzler; Nuno Carvalhais; Prabhat (Nat., 2019)

Optimizing I/O Performance of HPC Applications with Autotuning. - Babak Behzad; Surendra Byna; Prabhat; Marc Snir (ACM Trans. Parallel Comput., 2019)

Atmospheric Blocking Pattern Recognition in Global Climate Model Simulation Data. - Grzegorz Muszynski; Prabhat; Jan Balewski; Karthik Kashinath; Michael F. Wehner; Vitaliy Kurlin (ICPR, 2020)

Scaling of Union of Intersections for Inference of Granger Causal Networks from Observational Data. - Mahesh Balasubramanian 001; Trevor D. Ruiz; Brandon Cook 001; Prabhat; Sharmodeep Bhattacharyya; Aviral Shrivastava; Kristofer E. Bouchard (IPDPS, 2020)

MeshfreeFlowNet: a physics-constrained deep continuous space-time super-resolution framework. - Chiyu Max Jiang; Soheil Esmaeilzadeh; Kamyar Azizzadenesheli; Karthik Kashinath; Mustafa Mustafa; Hamdi A. Tchelepi; Philip Marcus; Prabhat; Anima Anandkumar (SC, 2020)

Track Seeding and Labelling with Embedded-space Graph Neural Networks. - Nicholas Choma; Daniel Murnane; Xiangyang Ju; Paolo Calafiura; Sean Conlon; Steven Farrell; Prabhat; Giuseppe Cerati; Lindsey Gray; Thomas Klijnsma; Jim Kowalkowski; Panagiotis Spentzouris; et al. (CoRR, 2020)

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. - Jinlong Wu; Karthik Kashinath; Adrian Albert; Dragos Chirila; Prabhat; Heng Xiao (J. Comput. Phys., 2020)

Characterizing Extreme Weather in a Changing Climate - Prabhat (2020)

Learning from learning machines: a new generation of AI technology to meet the needs of science. - Luca Pion-Tonachini; Kristofer E. Bouchard; Héctor García Martín; Sean Peisert; W. Bradley Holtz; Anil Aswani; Dipankar Dwivedi; Haruko M. Wainwright; Ghanshyam Pilania; Benjamin Nachman; Babetta L. Marrone; Nicola Falco; et al. (CoRR, 2021)

Impact of Discretization Techniques on Model Predictive Control of SRMs. - Prabhat; A. V. Ravi Teja (IECON, 2024)