21 citations · 33 across the 6 of their papers we have counts for
6 papers
Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models
Kenny Chowdhary, Chi Hoang, Kookjin Lee +1
In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationa…
Projection-based model reduction of dynamical systems using space-time subspace and machine learning
Chi Hoang, Kenny Chowdhary, Kookjin Lee +1
This paper considers the creation of parametric surrogate models for applications in science and engineering where the goal is to predict high-dimensional spatiotemporal output qua…
Daily Forecasting of New Cases for Regional Epidemics of Coronavirus Disease 2019 with Bayesian Uncertainty Quantification
Yen Ting Lin, Jacob Neumann, Ely Miller +7
To increase situational awareness and support evidence-based policy-making, we formulated two types of mathematical models for COVID-19 transmission within a regional population. O…
A stopping criterion for Markov chains when generating independent random graphs
J. Ray, A. Pinar, C. Seshadhri
Markov chains are convenient means of generating realizations of networks with a given (joint or otherwise) degree distribution, since they simply require a procedure for rewiring…
Decreasing the temporal complexity for nonlinear, implicit reduced-order models by forecasting
Kevin Carlberg, Jaideep Ray, Bart van Bloemen Waanders
Implicit numerical integration of nonlinear ODEs requires solving a system of nonlinear algebraic equations at each time step. Each of these systems is often solved by a Newton-lik…
Are we there yet? When to stop a Markov chain while generating random graphs
Jaideep Ray, Ali Pinar, C. Seshadhri
Markov chains are a convenient means of generating realizations of networks, since they require little more than a procedure for rewiring edges. If a rewiring procedure exists for…