Teresa Portone

Principal Member of the Technical Staff

Author profile picture

Principal Member of the Technical Staff

tporton@sandia.gov

(505) 494-3641

Sandia National Laboratories, New Mexico
 P.O. Box 5800
 Albuquerque, NM 87185-1327

Biography

Teresa joined Sandia in January 2020. Her research focuses on assessing and enhancing model prediction fidelity to inform high-consequence decisions for national security applications, especially in the presence of uncertainty. She has expertise in methods to characterize model-form uncertainty, sensitivity analysis, Bayesian methods, and multifidelity uncertainty quantification. She has worked on a range of applications, including disease modeling, nuclear waste repository modeling, ablation modeling for hypersonics, thermal batteries, and structural dynamics for aerospace problems.

Education

  • PhD, Computational Science, Engineering and Mathematics, University of Texas at Austin, December 2019
  • MS, Computational Science, Engineering and Mathematics, University of Texas at Austin, May 2016
  • BS, Mathematics, The University of Alabama, May 2013

Publications

  • Brooks, D., Swiler, L., Stein, E., Mariner, P., Basurto, E., Portone, T., Eckert, A., Leone, R., & Leone, R. (2022). Sensitivity analysis of generic deep geologic repository with focus on spatial heterogeneity induced by stochastic fracture network generation. Advances in Water Resources, 169. https://doi.org/10.1016/j.advwatres.2022.104310 Publication ID: 80187
  • Acquesta, E., Portone, T., Dandekar, R., Rackauckas, C., Bandy, R., Huerta, J., Dytzel, I., & Dytzel, I. (2022). Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology. https://doi.org/10.2172/1888443 Publication ID: 80247
  • Eldred, M., Adams, B., Geraci, G., Portone, T., Ridgway, E., Stephens, J., Wildey, T., & Wildey, T. (2022). Deployment of Multifidelity Uncertainty Quantification for Thermal Battery Assessment Part I: Algorithms and Single Cell Results. https://doi.org/10.2172/1885882 Publication ID: 80135
  • Swiler, L., Basurto, E., Brooks, D., Eckert, A., Leone, R., Mariner, P., Portone, T., Smith, M., & Smith, M. (2022). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2022). https://doi.org/10.2172/1884909 Publication ID: 80086
  • Portone, T. (2021). Computing bulk permeabilities for Task F Reference Case [Presentation]. https://www.osti.gov/biblio/1899241 Publication ID: 76695
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., Portone, T., Wildey, T., Rushdi, A.A., Seidl, D., & Seidl, D. (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact [Presentation]. https://www.osti.gov/biblio/1891078 Publication ID: 76127
  • Swiler, L., Basurto, E., Brooks, D., Eckert, A., Leone, R., Mariner, P., Portone, T., Smith, M., Stein, E., & Stein, E. (2021). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2021). https://doi.org/10.2172/1855018 Publication ID: 79851
  • Eldred, M., Geraci, G., Gorodetsky, A., Jakeman, J., Portone, T., & Portone, T. (2021). Efficient Deployment of Multifidelity Sampling Methods in Production Settings [Conference Presenation]. https://doi.org/10.2172/1882491 Publication ID: 79508
  • Michael, M., Geraci, G., Eldred, M., Portone, T., & Portone, T. (2021). Hybrid multi-level Monte Carlo polynomial chaos method for global sensitivity analysis [Conference Presenation]. https://doi.org/10.2172/1882333 Publication ID: 79476
  • Portone, T., Acquesta, E., Dandekar, R., Rackauckas, C., & Rackauckas, C. (2021). Learning Missing Mechanisms in a Dynamical System from a Subset of State Variable Observations [Conference Presenation]. https://doi.org/10.2172/1889367 Publication ID: 79360
  • Acquesta, E., Portone, T., & Portone, T. (2021). Assessing the Efficacy of Universal Differential Equations to Learn Missing Dynamics from a Subset of Observable State Variables [Conference Presenation]. https://doi.org/10.2172/1882346 Publication ID: 79285
  • Jakeman, J., Eldred, M., Geraci, G., Portone, T., Rushdi, A.A., Seidl, D., Smith, T., & Smith, T. (2021). Multi-fidelity Machine Learning [Conference Presenation]. https://doi.org/10.2172/1876608 Publication ID: 79162
  • Portone, T. (2021). Fracture statistics and integration into computational models [Presentation]. https://www.osti.gov/biblio/1869537 Publication ID: 78548
  • Portone, T. (2021). Multifidelity modeling for uncertainty assessment [Presentation]. https://www.osti.gov/biblio/1867791 Publication ID: 78398
  • Mariner, P., Brooks, D., Portone, T., & Portone, T. (2021). Repository Performance Metrics and Other Quantities of Interest [Presentation]. https://www.osti.gov/biblio/1866339 Publication ID: 78295
  • Maupin, K., Portone, T., & Portone, T. (2021). Use of Model Discrepancy and Model Selection as a Means of Informing Missing Physics [Conference Presenation]. https://doi.org/10.2172/1848042 Publication ID: 77386
  • Merritt, M., Geraci, G., Eldred, M., Portone, T., & Portone, T. (2021). Hybrid multi-level Monte Carlo polynomial chaos method for global sensitivity analysis [Conference Presenation]. https://doi.org/10.2172/1847581 Publication ID: 77380
  • Eldred, M., Gorodetsky, A., Geraci, G., Jakeman, J., Portone, T., & Portone, T. (2021). Recent Advances in Adaptive Refinement of (Regression-Based) Multifidelity Surrogates for UQ [Conference Presenation]. https://doi.org/10.2172/1847573 Publication ID: 77372
  • Portone, T., Swiler, L., Geraci, G., Eldred, M., & Eldred, M. (2021). Application of Multifidelity Uncertainty Quantification Methods to a Subsurface Transport Model [Conference Presenation]. https://doi.org/10.2172/1847219 Publication ID: 77339
  • Portone, T., Leone, R., & Leone, R. (2020). Flow and Transport Modeling in Fractured Crystalline Rock Using PFLOTRAN and dfnWorks: Crystalline Task F [Presentation]. https://www.osti.gov/biblio/1833141 Publication ID: 71831
  • Portone, T. (2020). Exploring Model-Form Uncertainty in ECPMs [Presentation]. https://www.osti.gov/biblio/1818051 Publication ID: 74687
  • DeRosa, S., Finley, P., Finley, M., Beyeler, W., Krofcheck, D., Frazier, C., Swiler, L., Portone, T., Acquesta, E., Austin, P., Levin, D., Taylor, R., Tremba, K., Makvandi, M., Hammer, A., Davis, C., & Davis, C. (2020). COVID-19 Medical Resource Demands [Presentation]. https://www.osti.gov/biblio/1807655 Publication ID: 74013
  • Swiler, L., Portone, T., & Portone, T. (2020). Uncertainty Analysis of a COVID-19 Medical Resource Model [Presentation]. https://www.osti.gov/biblio/1807392 Publication ID: 73743
  • Beyeler, W., Frazier, C., Swiler, L., Portone, T., Krofcheck, D., & Krofcheck, D. (2020). Treatment Model Design and Use [Presentation]. https://www.osti.gov/biblio/1783073 Publication ID: 73474
  • Swiler, L., Portone, T., Beyeler, W., & Beyeler, W. (2020). Uncertainty analysis of Resource Demand Model for Covid-19. https://doi.org/10.2172/1630395 Publication ID: 73459
  • Portone, T., Moser, R., & Moser, R. (2020). Characterizing model-form uncertainty in an inadequate model of anomalous transport [Conference Poster]. https://www.osti.gov/biblio/1783586 Publication ID: 72858
Showing 10 of 26 publications.