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
- Investigated Basin-Hopping and Minima Hopping combinations and modifications in order to accuratly and efficiently predict the ground state of Lennard-Jones systems
- Software for Global Optimization
- Global Optimization Database
- 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.