Publications

Quantifying Model-Form Uncertainty in the Absence of Data

International Modal Analysis Conference (IMAC) 2023

Teresa Portone, Kyle Daniel Neal, Angel Urbina, Erin Acquesta

Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations

8th European Congress on Computational Methods in Applied Sciences and Engineering

Teresa Portone, Erin Acquesta, Raj Dandekar, Chris Rackauckas

Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations

The 8th European Congress on Computational Methods in Applied Sciences and Engineering

Teresa Portone, Erin Acquesta, Chris Rackauckas, Raj Dandekar

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

SAGE Intrusion Detection System: Sensitivity Analysis Guided Explainability for Machine Learning

Michael Reed Smith, Erin Acquesta, Arlo L. Ames, Alycia Noel Carey, Christopher Roman Cuellar, Richard V. Field, Trevor Maxfield, Scott A. Mitchell, Elizabeth Susan Morris, Blake Cameron Moss, Megan Nyre-Yu, Ahmad Rushdi, Mallory Catherine Stites, Charles Smutz, Xin Zhou

https://www.osti.gov/search/identifier:1820253

Learning Missing Mechanisms in a Dynamical System from a Subset of State Variable Observations

16th U.S. National Congress on Computational Mechanics

Teresa Portone, Erin Acquesta, Raj Dandekar, Chris Rackauckas

Assessing the Efficacy of Universal Differential Equations to Learn Missing Dynamics from a Subset of Observable State Variables

Machine Learning Deep Learning Workshop

Erin Acquesta, Teresa Portone

Learning missing mechanisms in a dynamical system from a subset of state variable observations

16th U.S. National Congress on Computational Mechanics

Teresa Portone, Erin Acquesta, Ahmad Rushdi, Raj Dandekar, Chris Rackauckas

SAGE Advice? Assessing the Accuracy of ML Explanations for Model Credibility

NIST AI Assurance Leadership Team

Michael Reed Smith, Erin Acquesta, Richard V. Field, Trevor Maxfield, Blake Cameron Moss, Megan Nyre-Yu, Ahmad Rushdi, Charles Smutz, Mallory Catherine Stites

Time Series Dimension Reduction for Surrogate Models of Port Scanning Cyber Emulations

Erin Acquesta, Laura Painton Swiler, Ali Pinar

https://www.osti.gov/search/identifier:1673455

Assessing Global Sensitivity Analysis for Credibility in Machine Learning Explainability

SAMSIGlobal Sensitivity Analysis Working Group Meeting

Erin Acquesta, Michael Reed Smith, Richard V. Field, Trevor Maxfield, Ahmad Rushdi

https://www.osti.gov/search/identifier:1820562

COVID-19 Medical Resource Demands

DOE Presentation

Sean DeRosa, Patrick D. Finley, Melissa Finley, Walter Eugene Beyeler, Daniel Joseph Krofcheck, Christopher Rawls Frazier, Laura Painton Swiler, Teresa Portone, Erin Acquesta, Paula Austin, Drew Levin, Robert A. Taylor, Katherine Dorothy Tremba, Monear Makvandi, Ann Hammer, Chad E. Davis

https://www.osti.gov/search/identifier:1807655

Characterization of Partially Observed Epidemics - Application to COVID-19

Cosmin Safta, Jaideep Ray, Erin Acquesta, Thomas Anthony Catanach, Kamaljit Singh Chowdhary, Bert Debusschere, Edgar Galvan, Gianluca Geraci, Mohammad Khalil, Teresa Portone

https://www.osti.gov/search/identifier:1763554

Mathematics and Statistics at Sandia National Laboratories: Perspectives from an Engineering Scientist, Analyst, and Student Intern

NCSU Mathematics and Statistics Recruiting

Jordan Massad, Erin Acquesta, Joseph Lee Hart

https://www.osti.gov/search/identifier:1511133

Mathematics and Statistics at Sandia National Laboratories: Perspectives from an Engineering Scientist, Analyst, and Student Intern

NCSU Mathematics and Statistics Recruiting

Jordan Massad, Jordan Massad, Erin Acquesta, Erin Acquesta, Joseph Lee Hart, Joseph Lee Hart