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

  • Bandy, R.J., Portone, T., Morrison, R.E., & Morrison, R.E. (2023). Complex Couplings and Simple Springs: Analysis of Model-Form Error for Highly Nonlinear Oscillatory Systems [Conference Presenation]. https://doi.org/10.2172/2430532 Publication ID: 125808
  • Portone, T., Hart, J.L., White, R.D., & White, R.D. (2023). Quantifying Model Prediction Sensitivity to Model-Form Uncertainty [Conference Presenation]. https://doi.org/10.2172/2431876 Publication ID: 130500
  • Portone, T., Mariner, P.E., Bachman, W.B., Basurto, E., Leone, R.C., Stein, E., Swiler, L.P., & Swiler, L.P. (2022). Sensitivity analysis for deep geologic repository simulations in crystalline rock [Conference Presenation]. https://doi.org/10.2172/2006253 Publication ID: 121724
  • Portone, T. (2022). A stochastic operator model-form uncertainty representation of missing microstructural information [Conference Presenation]. https://doi.org/10.2172/2006212 Publication ID: 121560
  • Portone, T., Geraci, G., Swiler, L.P., Eldred, M., & Eldred, M. (2022). Multimodel Methods for Uncertainty Quantification of Repository Systems [Conference Presenation]. https://doi.org/10.2172/2005951 Publication ID: 120548
  • Brooks, D.M., Swiler, L.P., Stein, E., Mariner, P.E., Basurto, E., Portone, T., Eckert, A., Leone, R.C., & Leone, R.C. (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
  • Bachman, W.B., Portone, T., Dandekar, R., Rackauckas, C., Bandy, R.J., Huerta, J.G., 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.M., Geraci, G., Portone, T., Ridgway, E.M., Stephens, J.A., 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.P., Basurto, E., Brooks, D.M., Eckert, A., Leone, R.C., Mariner, P.E., Portone, T., Bachman, W.B., & Bachman, W.B. (2022). Uncertainty and Sensitivity Analysis Methods and Applications in the GDSA Framework (FY2022). https://doi.org/10.2172/1884909 Publication ID: 80086
  • Maupin, K.A., Ray, J., Bachman, W.B., Portone, T., & Portone, T. (2022). Informing Missing Physics with Model Form Error and Model Selection [Conference Presenation]. https://doi.org/10.2172/2004346 Publication ID: 116600
  • Portone, T., Swiler, L.P., Geraci, G., Eldred, M., & Eldred, M. (2022). Multimodel Methods for Uncertainty Quantification of Repository Systems [Conference Paper]. https://www.osti.gov/biblio/2004288 Publication ID: 116368
  • Bachman, W.B., Portone, T., Bandy, R.J., Dandekar, R., Rackauckas, C., & Rackauckas, C. (2022). Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations [Conference Presenation]. https://doi.org/10.2172/2004285 Publication ID: 116356
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics [Conference Paper]. https://www.osti.gov/biblio/2004284 Publication ID: 116352
  • Wildey, T., Geraci, G., Eldred, M., Jakeman, J.D., Davis, O., Portone, T., Yen, T.Y., Reuter, B.W., Gorodetsky, A., Rushdi, A., Schiavazzi, D., Partin, L., & Partin, L. (2022). Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ [Conference Presenation]. https://doi.org/10.2172/2003926 Publication ID: 114984
  • Mariner, P.E., Basurto, E., Brooks, D.M., Leone, R.C., Portone, T., Swiler, L., & Swiler, L. (2022). ADVANCED USE OF TRACERS IN REPOSITORY PERFORMANCE ASSESSMENT MODELING [Conference Paper]. https://www.osti.gov/biblio/2003689 Publication ID: 114052
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics [Conference Paper]. https://www.osti.gov/biblio/2003687 Publication ID: 114044
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Fracture Transmissivity Relationship on Repository Performance Characteristics [Conference Presenation]. https://doi.org/10.2172/2003735 Publication ID: 114232
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Fracture Transmissivity Relationship on Repository Performance Characteristics [Conference Paper]. https://doi.org/10.56952/ARMA-DFNE-22-0007 Publication ID: 114248
  • Portone, T., Bachman, W.B., Dandekar, R., Rackauckas, C., & Rackauckas, C. (2022). Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations [Conference Presenation]. https://doi.org/10.2172/2003410 Publication ID: 112968
  • Eldred, M., Geraci, G., Reuter, B.W., Portone, T., Jakeman, J.D., Gorodetsky, A.A., & Gorodetsky, A.A. (2022). Model Tuning for Multifidelity Sampling in Dakota [Conference Presenation]. https://doi.org/10.2172/2003065 Publication ID: 111692
  • Eldred, M., Geraci, G., Portone, T., Gorodetsky, A.A., Jakeman, J.D., & Jakeman, J.D. (2022). All-at-Once (and Bi-Level) Model Tuning for Multifidelity Sampling [Conference Presenation]. https://doi.org/10.2172/2002278 Publication ID: 110076
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Fracture Transmissivity Relationship on Repository Performance Characteristics [Conference Paper]. https://doi.org/10.56952/ARMA-DFNE-22-0007 Publication ID: 109972
  • Swiler, L.P., Portone, T., Mariner, P.E., Leone, R., Brooks, D.M., Seidl, D.T., Debusschere, B.J., Berg, T., & Berg, T. (2022). Use of a machine learning model for a constitutive chemistry model within a groundwater flow and transport application modeling nuclear fuel degradation in a waste repository [Conference Presenation]. https://doi.org/10.2172/2002228 Publication ID: 109876
  • Merritt, M., Geraci, G., Portone, T., Eldred, M., & Eldred, M. (2022). Hybrid multilevel Monte Carlo polynomial chaos method for global sensitivity analysis [Conference Presenation]. https://doi.org/10.2172/2002182 Publication ID: 109692
  • Portone, T., Moser, R.D., & Moser, R.D. (2022). Physics-constrained Bayesian inference of an uncertain operator in the sparse-data regime [Conference Presenation]. https://doi.org/10.2172/2002150 Publication ID: 109568
  • Mariner, P.E., Basurto, E., Brooks, D.M., Leone, R.C., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Advanced Use of Tracers in Repository Performance Assessment Modeling [Conference Paper]. https://www.osti.gov/biblio/2002123 Publication ID: 109468
  • Portone, T., Eldred, M., Geraci, G., Swiler, L.P., & Swiler, L.P. (2022). Multimodel Methods for Uncertainty Quantification of Repository Systems [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181554658&origin=inward Publication ID: 114276
  • Brooks, D.M., Swiler, L.P., Mariner, P.E., Portone, T., Basurto, E., Leone, R.C., & Leone, R.C. (2022). Sensitivity and Uncertainty Analysis of FMD Model Choice for a Generic Crystalline Repository [Conference Presenation]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://doi.org/10.2172/2005997 Publication ID: 120724
  • Mariner, P.E., Basurto, E., Brooks, D.M., Leone, R.C., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Use of Virtual Tracers in Repository Performance Assessment Modeling [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181557302&origin=inward Publication ID: 116320
  • Brooks, D.M., Swiler, L.P., Mariner, P.E., Portone, T., Basurto, E., Leone, R.C., & Leone, R.C. (2022). Sensitivity and Uncertainty Analysis of FMD Model Choice for a Generic Crystalline Repository [Conference Proceeding]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181554518&origin=inward Publication ID: 116360
  • Bachman, W.B., Portone, T., Swiler, L.P., & Swiler, L.P. (2022). Effects of Discrete Fracture Network Modeling Choices on Repository Performance Characteristics [Conference Presenation]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://doi.org/10.2172/2006048 Publication ID: 120920
  • 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.A., Jakeman, J.D., Portone, T., Wildey, T., Rushdi, A., Seidl, D.T., & Seidl, D.T. (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.P., Basurto, E., Brooks, D.M., Eckert, A., Leone, R.C., Mariner, P.E., Portone, T., Bachman, W.B., 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.A., Jakeman, J.D., 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., Bachman, W.B., 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
  • Bachman, W.B., 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.D., Eldred, M., Geraci, G., Portone, T., Rushdi, A., Seidl, D.T., Smith, T.M., & Smith, T.M. (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.E., Brooks, D.M., Portone, T., & Portone, T. (2021). Repository Performance Metrics and Other Quantities of Interest [Presentation]. https://www.osti.gov/biblio/1866339 Publication ID: 78295
  • Portone, T., Swiler, L.P., 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
  • Eldred, M., Gorodetsky, A.A., Geraci, G., Jakeman, J.D., 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
  • Maupin, K.A., 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
  • Portone, T., Leone, R.C., & Leone, R.C. (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
  • Swiler, L.P., Basurto, E., Brooks, D.M., Eckert, A., Mariner, P.E., Portone, T., Stein, E., & Stein, E. (2020). Advances in Uncertainty and Sensitivity Analysis Methods and Applications in GDSA Framework. https://doi.org/10.2172/1671381 Publication ID: 100180
  • Portone, T. (2020). Exploring Model-Form Uncertainty in ECPMs [Presentation]. https://www.osti.gov/biblio/1818051 Publication ID: 74687
  • Derosa, S., Finley, P.D., Finley, M., Beyeler, W.E., Krofcheck, D.J., Frazier, C.R., Swiler, L.P., Portone, T., Bachman, W.B., Austin, P., Levin, D., Bachman, W.B., Tremba, K.D., Makvandi, M., Hammer, A.E., Davis, C.E., & Davis, C.E. (2020). COVID-19 Medical Resource Demands [Presentation]. https://www.osti.gov/biblio/1807655 Publication ID: 74013
  • Safta, C., Ray, J., Bachman, W.B., Catanach, T.A., Chowdhary, K., Debusschere, B.J., Galvan, E., Geraci, G., Khalil, M., Portone, T., & Portone, T. (2020). Characterization of Partially Observed Epidemics – Application to COVID-19. https://doi.org/10.2172/1763554 Publication ID: 103028
  • Beyeler, W.E., Frazier, C.R., Krofcheck, D.J., Swiler, L.P., Portone, T., Klise, K.A., & Klise, K.A. (2020). Uncertainty Analysis Framework for the Hospital Resource Supply Model for Covid-19. https://doi.org/10.2172/1763544 Publication ID: 102996
  • Swiler, L.P., 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.E., Frazier, C.R., Swiler, L.P., Portone, T., Krofcheck, D.J., & Krofcheck, D.J. (2020). Treatment Model Design and Use [Presentation]. https://www.osti.gov/biblio/1783073 Publication ID: 73474
  • Swiler, L.P., Portone, T., Beyeler, W.E., & Beyeler, W.E. (2020). Uncertainty analysis of Resource Demand Model for Covid-19. https://doi.org/10.2172/1630395 Publication ID: 73459
  • Portone, T., Moser, R.D., & Moser, R.D. (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 56 publications.