61 citations · 121 across the 8 of their papers we have counts for
8 papers
When Artificial Parameter Evolution Gets Real: Particle Filtering for Time-Varying Parameter Estimation in Deterministic Dynamical Systems
Andrea Arnold
Estimating and quantifying uncertainty in unknown system parameters from limited data remains a challenging inverse problem in a variety of real-world applications. While many appr…
Identification of Tissue Optical Properties During Thermal Laser-Tissue Interactions: An Ensemble Kalman Filter-Based Approach
Andrea Arnold, Loris Fichera
In this paper, we propose a computational framework to estimate the physical properties that govern the thermal response of laser-irradiated tissue. We focus in particular on two q…
Fourier Series-Based Approximation of Time-Varying Parameters in Ordinary Differential Equations
Anna Fitzpatrick, Molly Folino, Andrea Arnold
Many real-world systems modeled using differential equations involve unknown or uncertain parameters. Standard approaches to address parameter estimation inverse problems in this s…
Analyzing the Effects of Observation Function Selection in Ensemble Kalman Filtering for Epidemic Models
Leah Mitchell, Andrea Arnold
The Ensemble Kalman Filter (EnKF) is a popular sequential data assimilation method that has been increasingly used for parameter estimation and forecast prediction in epidemiologic…
Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
Kayleigh Campbell, Laura Staugler, Andrea Arnold
The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological dynamics of a single neuron. While applying a constant current to the system results in a…
Practical Identifiability and Uncertainty Quantification of a Pulsatile Cardiovascular Model
Andrew D. Marquis, Andrea Arnold, Caron Dean +2
Mathematical models are essential tools to study how the cardiovascular system maintains homeostasis. The utility of such models is limited by the accuracy of their predictions, wh…
An approach to periodic, time-varying parameter estimation using nonlinear filtering
Andrea Arnold, Alun L. Lloyd
Many systems arising in biological applications are subject to periodic forcing. In these systems the forcing parameter is not only time-varying but also known to have a periodic s…
Vectorized and Parallel Particle Filter SMC Parameter Estimation for Stiff ODEs
Andrea Arnold, Daniela Calvetti, Erkki Somersalo
Particle filter (PF) sequential Monte Carlo (SMC) methods are very attractive for the estimation of parameters of time dependent systems where the data is either not all available…