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Publication	Type	Year
Learning to Parameterize a Stochastic Processing Using Neuromorphic Data Generation International Conference on Neuromorphic Systems William Mark Severa, John Darby Smith, Richard B. Lehoucq, James Bradley Aimone	Conference Presentation – 2022 Conference Presentation	2022
Neural Mini-Apps: Lighter, Faster Diagnostic Tests for Neuromorphic Computing James Bradley Aimone, Suma George Cardwell, Frances S. Chance, Srideep Musuvathy, Fredrick Rothganger, William Mark Severa, John Darby Smith, Corinne Michelle Teeter, Craig Michael Vineyard, Felix Wang	Marketing or Promotional – 2022 Marketing or Promotional	2022
Learning to Parameterize a Stochastic Process Using Neuromorphic Data Generation International Conference on Neuromorphic Systems William Mark Severa, John Darby Smith, Richard B. Lehoucq, James Bradley Aimone	Conference Paper – 2022 Conference Paper	2022
Stochastic magnetic actuated random transducer devices based on perpendicular magnetic tunnel junctions Mmm 2022 L. Rehm, C.C.M Capriata, Shashank Misra, John Darby Smith, M. Pinarbasi, B. G. Malm, A. D. Kent	Abstract – 2022 Abstract	2022
Particle approach to inverse problems via gradient-based optimization CIS Research Foundation External Review Stephen D Bond, Brian C. Franke, Richard B. Lehoucq, Aaron Jeffrey Olson, John Darby Smith, Scott McKinley, Petr Plechac	Display or Poster (non-conference) – 2022 Display or Poster (non-conference)	2022
Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling International Symposium on Roadmappign Devices and Systems John Darby Smith	Conference Presentation – 2022 Conference Presentation	2022
Neural Mini-Apps as a Tool for Neuromorphic Computing Insight Neuro-Inspired Computational Elements (NICE) Workshop 2022 Craig Michael Vineyard, James Bradley Aimone, Suma George Cardwell, Frances S. Chance, Srideep Musuvathy, Fredrick Rothganger, John Darby Smith, William Mark Severa, Corinne Michelle Teeter, Felix Wang	Conference Presentation – 2022 Conference Presentation	2022
Neural Mini-Apps as a Tool for Neuromorphic Computing Insight The 9th Annual Neuro-Inspired Computational Elements (NICE) workshop Craig Michael Vineyard, Suma George Cardwell, Frances S. Chance, Srideep Musuvathy, Fredrick Rothganger, William Mark Severa, John Darby Smith, Corinne Michelle Teeter, Felix Wang, James Bradley Aimone	Conference Proceeding – 2022 Conference Proceeding	2022
Neuromorphic Architectures: Efficient and Parallel Post-Moore Scientific Computing Potential SIAM Parallel Processing 2022 John Darby Smith, William Mark Severa	Conference Presentation – 2022 Conference Presentation	2022
Neuromorphic scaling advantages for energy-efficient random walk computations Nature Electronics John Darby Smith, Aaron Jamison Hill, Reeder Leah, Brian C. Franke, Richard B. Lehoucq, Ojas D. Parekh, William Mark Severa, James Bradley Aimone https://www.osti.gov/search/identifier:1845387	Journal Article – 2022 Journal Article	2022
CSRI Summer Proceedings 2021 John Darby Smith, Edgar Galvan https://www.osti.gov/search/identifier:1855061	Report – 2022 Report	2022
CSRI Summer Proceedings 2021 John Darby Smith, Edgar Galvan https://www.osti.gov/search/identifier:1836402	Report – 2021 Report	2021
Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling International Conference on Rebooting Computing John Darby Smith, Richard B. Lehoucq, James Bradley Aimone, William Mark Severa	Conference Paper – 2021 Conference Paper	2021
Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling International Conference on Rebooting Computing John Darby Smith, James Bradley Aimone, William Mark Severa, Richard B. Lehoucq	Conference Presentation – 2021 Conference Presentation	2021
The ASC Advanced Machine Learning Initiative at Sandia National Laboratories: FY21 Accomplishments and FY22 Plans ASC AMLI Program Review Ron A. Oldfield, Sharlotte LorraineBolyard Kramer, Ahmad Rushdi, Erin Acquesta, John M Emery, Paul Allen Kuberry, Jaideep Ray, Sarah Ackerman, Eric Christopher Cyr, Gary Joseph Saavedra, Clayton Hughes, Suma George Cardwell, John Darby Smith	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Neural-Inspired Computation for Scientific Computing Berry College Mathematics and Computer Science Seminar (Virtual) John Darby Smith https://www.osti.gov/search/identifier:1860609	Presentation (non-conference) – 2021 Presentation (non-conference)	2021
Learning to Solve an Inverse Problem Using Neuromorphic Data Generation Smoky Mountains Conference John Darby Smith, William Mark Severa, Richard B. Lehoucq, James Bradley Aimone	Abstract – 2021 Abstract	2021
Neural Network Approaches for Enabling Automatic Target Recognition WorkshopUncertainty Management and Machine Learning in Engineering Applications Craig Michael Vineyard, Ryan David Melzer, Srideep Musuvathy, John Richards, William Mark Severa, John Darby Smith https://www.osti.gov/search/identifier:1831567	Conference Presentation – 2020 Conference Presentation	2020
Neuromorphic scaling advantages for energy-efficient random walk computations John Darby Smith, Aaron Jamison Hill, Leah Reeder, Brian C. Franke, Richard B. Lehoucq, Ojas D. Parekh, William Mark Severa, James Bradley Aimone https://www.osti.gov/search/identifier:1671377	SAND Report – 2020 SAND Report	2020
Solving a steady-state pde using spiking networks and neuromorphic hardware International Conference on Neuromorphic Systems John Darby Smith, William Mark Severa, Aaron Jamison Hill, Leah Reeder, Ojas D. Parekh, Brian C. Franke, Richard B. Lehoucq, James Bradley Aimone https://www.osti.gov/search/identifier:1813714	Conference Paper – 2020 Conference Paper	2020
Solving a steady-state pde using spiking networks and neuromorphic hardware International Conference on Neuromorphic Systems John Darby Smith, William Mark Severa, Aaron Jamison Hill, Leah Evelyn Reeder, Brian C. Franke, Richard B. Lehoucq, Ojas D. Parekh, James Bradley Aimone https://www.osti.gov/search/identifier:1808426	Conference Paper – 2020 Conference Paper	2020
Sensitivity analysis for Monte Carlo simulations of integro-partial differential equations Coda2020 Stephen D Bond, Brian C. Franke, Richard B. Lehoucq, John Darby Smith https://www.osti.gov/search/identifier:1765803	Conference Paper – 2020 Conference Paper	2020
Leveraging Random Walks to Solve IPDEs on Spiking Neuromorphic Hardware SIAM Parallel Processing John Darby Smith, James Bradley Aimone, William Mark Severa https://www.osti.gov/search/identifier:1765110	Conference Paper – 2020 Conference Paper	2020
Sensitivity analysis for Monte Carlo simulations of integro-partial differential equations CoDA 2020 Richard B. Lehoucq, Brian C. Franke, Shannon Bond, John Darby Smith https://www.osti.gov/search/identifier:1764792	Conference Paper – 2020 Conference Paper	2020
Solving IPDEs on Spiking Neuromorphic Hardware Joint Math Meetings John Darby Smith, James Bradley Aimone, Brian C. Franke, Aaron Jamison Hill, Richard B. Lehoucq, Ojas D. Parekh, Leah Evelyn Reeder, William Mark Severa https://www.osti.gov/search/identifier:1761032	Conference Paper – 2020 Conference Paper	2020
Document Title	Type	Year

Results 1–25 of 28

Publications

Learning to Parameterize a Stochastic Processing Using Neuromorphic Data Generation

Neural Mini-Apps: Lighter, Faster Diagnostic Tests for Neuromorphic Computing

Learning to Parameterize a Stochastic Process Using Neuromorphic Data Generation

Stochastic magnetic actuated random transducer devices based on perpendicular magnetic tunnel junctions

Particle approach to inverse problems via gradient-based optimization

Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling

Neural Mini-Apps as a Tool for Neuromorphic Computing Insight

Neural Mini-Apps as a Tool for Neuromorphic Computing Insight

Neuromorphic Architectures: Efficient and Parallel Post-Moore Scientific Computing Potential

Neuromorphic scaling advantages for energy-efficient random walk computations

CSRI Summer Proceedings 2021

CSRI Summer Proceedings 2021

Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling

Assessing a Neuromorphic Platform for use in Scientific Stochastic Sampling

The ASC Advanced Machine Learning Initiative at Sandia National Laboratories: FY21 Accomplishments and FY22 Plans

Neural-Inspired Computation for Scientific Computing

Learning to Solve an Inverse Problem Using Neuromorphic Data Generation

Neural Network Approaches for Enabling Automatic Target Recognition

Neuromorphic scaling advantages for energy-efficient random walk computations

Solving a steady-state pde using spiking networks and neuromorphic hardware

Solving a steady-state pde using spiking networks and neuromorphic hardware

Sensitivity analysis for Monte Carlo simulations of integro-partial differential equations

Leveraging Random Walks to Solve IPDEs on Spiking Neuromorphic Hardware

Sensitivity analysis for Monte Carlo simulations of integro-partial differential equations

Solving IPDEs on Spiking Neuromorphic Hardware