Current Research

  • Uncertainty Quantification and Validation
    • Representing and learning the uncertainties in complex, physics-based computational models. However, the form of the computational model greatly influences our approach to quantifying uncertainties. If the model is defined by a system of equations, we explore learning the model-form error through embedded model corrections. If the model is defined by black-box observations, we explore a posterior representations of uncertainties.
    • Building data-driven models that adhere to physical constraints
    • Calibration and validation algorithms

Current Projects

  • ACCurate and Reliable Uncertainty Estimates (ACCRUE)
    • Generating probabilistic forecasts by augmenting black-box outputs with an uncertainty distribution.
    • Extending to non-Guassian distributions.
  • Model-form error in ODEs with missing variables
  • Model-form error in systems with missing variables and simplified physics

Previous Projects

  • Validating Neural-Network-Corrected Dynamical Systems
    • Calibrated and validated a neural-network-corrected compartmental disease model.
    • Investigated how calibration data and the neural network architecture affected the validation time horizon.
  • Global Optimization of Chemical Systems
  • Modeling Microstructure Evolution during Additive Manufacturing
    • Employed a data science approach and machine learning to simulated additive manufacturing

Publications

  • R. Bandy and R. Morrison, Stochastic Model Corrections for Reduced Lotka-Volterra Models Exhibiting Mutual, Competitive, and Predatory Interactions, in Chaos: An Interdisciplinary Journal of Nonlinear Science, In revision.
  • R. Bandy and R. Morrison, Quantifying Model Form Uncertainty in Spring-Mass-Damper Systems, in Conference Proceedings of the Society for Experimental Mechanics Series, Best Paper in Model Validation & Uncertainty Quantification.

Technical Reports

  • R. Bandy, T. Portone, and M. Sands, Quantifying and Reducing Uncertainties in Ablation Models for Hypersonic Flight, in Computer Science Research Institute (CSRI) Summer Proceedings 2023, Sandia National Laboratories, In review.
  • R. Bandy, T. Portone, and E. Acquesta, Validating Neural-Network-Corrected Dynamical Systems, in CSRI Summer Proceedings 2022, S.K. Seritan and J.D. Smith, eds., Technical Report SAND2022-10280R, Sandia National Laboratories, 2022, pp. 14–30.
  • E. Acquesta, T. Portone, R. Dandekar, C. Rackaukas, R. Bandy, G. Huerta, and I. Dytzel, Model-form Epistemic Uncertainty Quantification for Modeling with Differential Equations: Application to Epidemiology, in Sandia Report, Technical Report SAND2022-12823, Sandia National Laboratories, 2022, pp. 1–44.

Presentations

  • Complex Couplings and Simple Springs: Analysis of Model-Form Error for Highly Nonlinear Oscillatory Systems. MS 407.2 session presented at 17th U.S. National Congress on Computational Mechanics (USNCCM); July 26th, 2023; Albuquerque, NM.
  • Skewed Uncertainty Estimates for Deterministic Predictions. Poster session for junior researchers presented at Space Weather with Quantified Uncertainties Spring Meeting 2023; March 10th, 2023; Cambridge, MA.
  • Quantifying Model Form Uncertainty in Spring-Mass-Damper Systems. Session 23 presented at SEM IMAC-XLI; February 14th, 2023; Austin, TX.
  • Model Correction and Validation of Reduced Lotka-Volterra Models. MS 104 session presented at SIAM Conference on Uncertainty Quantification; April 14th, 2022; Atlanta, GA.
  • Model Correction and Validation of Reduced Lotka-Volterra Models. Poster session presented at SIAM Conference on Applications of Dynamical Systems; May 26th, 2021; Virtual.
  • Investigating Methodology for Global Optimization. Poster session presented at the AAAS Annual Meeting; February 18th, 2018; Austin, TX.
  • Investigating Methodology for Global Optimization. Poster session presented at: Institute of Pure and Applied Mathematics workshop on Optimization and Optimal Control for Complex Energy and Property Landscapes; October 2nd, 2017; Los Angeles, CA.