Multi point strain and temperature sensing based on Brillouin optical time domain reflectometry. - Tianrong Chen; X. Xu; Nageswara Lalam; Wai Pang Ng; Peter Harrington (CSNDSP, 2018)

Establishing the flightpath of a quadcopter drone from the relative angular velocity of the four rotors. - Peter Harrington; Tianrong Chen; Wai Pang Ng (CSNDSP, 2018)

Autonomous drone network: non-intrusive control and indoor formation positioning. - Peter Harrington (2019)

Autonomous drone control within a Wi-Fi network. - Peter Harrington; Wai Pang Ng; Richard Binns (CSNDSP, 2020)

Self-Supervised Representation Learning for Astronomical Images. - Md. Abul Hayat; George Stein; Peter Harrington; Zarija Lukic; Mustafa Mustafa (CoRR, 2020)

Integrating Radio-Over-Fiber Communication System and BOTDR Sensor System. - Wai Pang Ng; Nageswara Lalam; Xuewu Dai; Qiang Wu 005; Yong Qing Fu; Peter Harrington; Nathan J. Gomes; Chao Lu 001 (Sensors, 2020)

Estimating Galactic Distances From Images Using Self-supervised Representation Learning. - Md. Abul Hayat; Peter Harrington; George Stein; Zarija Lukic; Mustafa Mustafa (CoRR, 2021)

Fast, high-fidelity Lyman α forests with convolutional neural networks. - Peter Harrington; Mustafa Mustafa; Max Dornfest; Benjamin Horowitz; Zarija Lukic (CoRR, 2021)

Mining for strong gravitational lenses with self-supervised learning. - George Stein; Jacqueline Blaum; Peter Harrington; Tomislav Medan; Zarija Lukic (CoRR, 2021)

Self-supervised similarity search for large scientific datasets. - George Stein; Peter Harrington; Jacqueline Blaum; Tomislav Medan; Zarija Lukic (CoRR, 2021)

Co-design Center for Exascale Machine Learning Technologies (ExaLearn). - Francis J. Alexander; James A. Ang; Jenna A. Bilbrey; Jan Balewski; Tiernan Casey; Ryan Chard; Jong Choi 001; Sutanay Choudhury; Bert J. Debusschere; Anthony M. DeGennaro; Nikoli Dryden; J. Austin Ellis; et al. (Int. J. High Perform. Comput. Appl., 2021)

The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism. - Yosuke Oyama; Naoya Maruyama; Nikoli Dryden; Erin McCarthy; Peter Harrington; Jan Balewski; Satoshi Matsuoka; Peter Nugent; Brian Van Essen (IEEE Trans. Parallel Distributed Syst., 2021)

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators. - Jaideep Pathak; Shashank Subramanian; Peter Harrington; Sanjeev Raja; Ashesh Chattopadhyay; Morteza Mardani; Thorsten Kurth; David Hall; Zongyi Li; Kamyar Azizzadenesheli; Pedram Hassanzadeh; Karthik Kashinath; et al. (CoRR, 2022)

Generative Modeling of High-resolution Global Precipitation Forecasts. - James Duncan 002; Shashank Subramanian; Peter Harrington (CoRR, 2022)

Efficient Segment-Level Waveform Anomaly Detection for Memory Devices. - Ching-Lun Tai; Peter Harrington (ICC, 2023)

Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior. - Shashank Subramanian; Peter Harrington; Kurt Keutzer; Wahid Bhimji; Dmitriy Morozov; Michael W. Mahoney; Amir Gholami (NeurIPS, 2023)

FourCastNet: Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators. - Thorsten Kurth; Shashank Subramanian; Peter Harrington; Jaideep Pathak; Morteza Mardani; David Hall; Andrea Miele; Karthik Kashinath; Anima Anandkumar (PASC, 2023)

Towards Stability of Autoregressive Neural Operators. - Michael McCabe; Peter Harrington; Shashank Subramanian; Jed Brown (Trans. Mach. Learn. Res., 2023)

Comprehensive Performance Modeling and System Design Insights for Foundation Models. - Shashank Subramanian; Ermal Rrapaj; Peter Harrington; Smeet Chheda; Steven Farrell; Brian Austin; Samuel Williams 001; Nicholas J. Wright; Wahid Bhimji (SC Workshops, 2024)

A Practical Probabilistic Benchmark for AI Weather Models. - Noah D. Brenowitz; Yair Cohen; Jaideep Pathak; Ankur Mahesh; Boris Bonev; Thorsten Kurth; Dale R. Durran; Peter Harrington; Michael S. Pritchard (CoRR, 2024)

Analyzing and Exploring Training Recipes for Large-Scale Transformer-Based Weather Prediction. - Jared D. Willard; Peter Harrington; Shashank Subramanian; Ankur Mahesh; Travis A. O'Brien; William D. Collins (CoRR, 2024)

Huge Ensembles Part II: Properties of a Huge Ensemble of Hindcasts Generated with Spherical Fourier Neural Operators. - Ankur Mahesh; William D. Collins; Boris Bonev; Noah D. Brenowitz; Yair Cohen; Peter Harrington; Karthik Kashinath; Thorsten Kurth; Joshua North; Travis A. O'Brien; Michael S. Pritchard; David Pruitt; et al. (CoRR, 2024)

Huge Ensembles Part I: Design of Ensemble Weather Forecasts using Spherical Fourier Neural Operators. - Ankur Mahesh; William D. Collins; Boris Bonev; Noah D. Brenowitz; Yair Cohen; Joshua Elms; Peter Harrington; Karthik Kashinath; Thorsten Kurth; Joshua North; Travis A. O'Brien; Michael S. Pritchard; et al. (CoRR, 2024)

Kilometer-Scale Convection Allowing Model Emulation using Generative Diffusion Modeling. - Jaideep Pathak; Yair Cohen; Piyush Garg; Peter Harrington; Noah D. Brenowitz; Dale R. Durran; Morteza Mardani; Arash Vahdat; Shaoming Xu; Karthik Kashinath; Michael S. Pritchard (CoRR, 2024)

Hierarchical Conditional Multi-Task Learning for Streamflow Modeling. - Shaoming Xu; Arvind Renganathan; Ankush Khandelwal; Rahul Ghosh; Xiang Li; Licheng Liu; Kshitij Tayal; Peter Harrington; Xiaowei Jia; Zhenong Jin; John Nieber; Vipin Kumar 001 (CoRR, 2024)