108 citations · 525 across the 62 of their papers we have counts for
9 papers · 1 filter
Dynamic Response Optimization of Complex Multibody Systems in a Penalty Formulation using Adjoint Sensitivity
Yitao Zhu, Daniel Dopico, Corina Sandu +1
Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computationa…
Approximate Exponential Algorithms to Solve the Chemical Master Equation
Azam S. Zavar Moosavi, Adrian Sandu
This paper discusses new simulation algorithms for stochastic chemical kinetics that exploit the linearity of the chemical master equation and its matrix exponential exact solution…
A-posteriori error estimates for inverse problems
Vishwas Rao, Adrian Sandu
Inverse problems use physical measurements along with a computational model to estimate the parameters or state of a system of interest. Errors in measurements and uncertainties in…
Efficient approximation of sparse Jacobians for time-implicit reduced order models
Răzvan Ştefănescu, Adrian Sandu
This paper introduces a sparse matrix discrete interpolation method to effectively compute matrix approximations in the reduced order modeling framework. The sparse algorithm devel…
High Order Implicit-Explicit General Linear Methods with Optimized Stability Regions
Hong Zhang, Adrian Sandu, Sebastien Blaise
In the numerical solution of partial differential equations using a method-of-lines approach, the availability of high order spatial discretization schemes motivates the developmen…
Optimization of Vehicle Dynamics based on Multibody Models using Adjoint Sensitivity Analysis
Yitao Zhu, Corina Sandu, Daniel Dopico +1
Multibody dynamics simulations have become widely used tools for vehicle systems analysis and design. As this approach evolves, it becomes able to provide additional information fo…
A Sampling Filter for Non-Gaussian Data Assimilation
Ahmed Attia, Adrian Sandu
Data assimilation combines information from models, measurements, and priors to estimate the state of a dynamical system such as the atmosphere. The Ensemble Kalman filter (EnKF) i…
Comparison of POD reduced order strategies for the nonlinear 2D Shallow Water Equations
Răzvan Ştefănescu, Adrian Sandu, Ionel M. Navon
This paper introduces tensorial calculus techniques in the framework of Proper Orthogonal Decomposition (POD) to reduce the computational complexity of the reduced nonlinear terms.…
POD/DEIM Reduced-Order Strategies for Efficient Four Dimensional Variational Data Assimilation
Răzvan Ştefănescu, Adrian Sandu, Ionel Michael Navon
This work studies reduced order modeling (ROM) approaches to speed up the solution of variational data assimilation problems with large scale nonlinear dynamical models. It is show…