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You searched for +publisher:"Virginia Tech" +contributor:("Sandu, Adrian"). Showing records 1 – 30 of 52 total matches.

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Virginia Tech

1. Cui, Jing. Visualization of the Budding Yeast Cell Cycle.

Degree: MS, Computer Science, 2017, Virginia Tech

 The cell cycle of budding yeast is controlled by a complex chemically reacting network. Many mathematical models have been proposed to unravel its molecular mechanism.… (more)

Subjects/Keywords: Budding yeast cell cycle; Deterministic model; MUTANTS; HYBRID MODEL; VISUALIZATION

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APA (6th Edition):

Cui, J. (2017). Visualization of the Budding Yeast Cell Cycle. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78815

Chicago Manual of Style (16th Edition):

Cui, Jing. “Visualization of the Budding Yeast Cell Cycle.” 2017. Masters Thesis, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/78815.

MLA Handbook (7th Edition):

Cui, Jing. “Visualization of the Budding Yeast Cell Cycle.” 2017. Web. 29 Jan 2020.

Vancouver:

Cui J. Visualization of the Budding Yeast Cell Cycle. [Internet] [Masters thesis]. Virginia Tech; 2017. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/78815.

Council of Science Editors:

Cui J. Visualization of the Budding Yeast Cell Cycle. [Masters Thesis]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/78815


Virginia Tech

2. D'Augustine, Anthony Frank. MATLODE: A MATLAB ODE Solver and Sensitivity Analysis Toolbox.

Degree: MS, Computer Science, 2018, Virginia Tech

 Sensitivity analysis quantifies the effect that of perturbations of the model inputs have on the model's outputs. Some of the key insights gained using sensitivity… (more)

Subjects/Keywords: ODE Solver; Tangent Linear Model; Adjoint Model; Sensitivity Analysis; Software

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APA (6th Edition):

D'Augustine, A. F. (2018). MATLODE: A MATLAB ODE Solver and Sensitivity Analysis Toolbox. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83081

Chicago Manual of Style (16th Edition):

D'Augustine, Anthony Frank. “MATLODE: A MATLAB ODE Solver and Sensitivity Analysis Toolbox.” 2018. Masters Thesis, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/83081.

MLA Handbook (7th Edition):

D'Augustine, Anthony Frank. “MATLODE: A MATLAB ODE Solver and Sensitivity Analysis Toolbox.” 2018. Web. 29 Jan 2020.

Vancouver:

D'Augustine AF. MATLODE: A MATLAB ODE Solver and Sensitivity Analysis Toolbox. [Internet] [Masters thesis]. Virginia Tech; 2018. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/83081.

Council of Science Editors:

D'Augustine AF. MATLODE: A MATLAB ODE Solver and Sensitivity Analysis Toolbox. [Masters Thesis]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83081


Virginia Tech

3. Radcliffe, Nicholas Ryan. Adjusting Process Count on Demand for Petascale Global Optimization.

Degree: MS, Computer Science, 2011, Virginia Tech

 There are many challenges that need to be met before efficient and reliable computation at the petascale is possible. Many scientific and engineering codes running… (more)

Subjects/Keywords: Petascale computing; Global optimization; Message Passing Interface (MPI); Dynamic process count

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APA (6th Edition):

Radcliffe, N. R. (2011). Adjusting Process Count on Demand for Petascale Global Optimization. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/36349

Chicago Manual of Style (16th Edition):

Radcliffe, Nicholas Ryan. “Adjusting Process Count on Demand for Petascale Global Optimization.” 2011. Masters Thesis, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/36349.

MLA Handbook (7th Edition):

Radcliffe, Nicholas Ryan. “Adjusting Process Count on Demand for Petascale Global Optimization.” 2011. Web. 29 Jan 2020.

Vancouver:

Radcliffe NR. Adjusting Process Count on Demand for Petascale Global Optimization. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/36349.

Council of Science Editors:

Radcliffe NR. Adjusting Process Count on Demand for Petascale Global Optimization. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/36349


Virginia Tech

4. Gou, Tianyi. Computational Tools for Chemical Data Assimilation with CMAQ.

Degree: MS, Computer Science, 2010, Virginia Tech

 The Community Multiscale Air Quality (CMAQ) system is the Environmental Protection Agency's main modeling tool for atmospheric pollution studies. CMAQ-ADJ, the adjoint model of CMAQ,… (more)

Subjects/Keywords: Data Assimilation; Chemical Transport Models; Adjoint Sensitivity Analysis

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APA (6th Edition):

Gou, T. (2010). Computational Tools for Chemical Data Assimilation with CMAQ. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/31017

Chicago Manual of Style (16th Edition):

Gou, Tianyi. “Computational Tools for Chemical Data Assimilation with CMAQ.” 2010. Masters Thesis, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/31017.

MLA Handbook (7th Edition):

Gou, Tianyi. “Computational Tools for Chemical Data Assimilation with CMAQ.” 2010. Web. 29 Jan 2020.

Vancouver:

Gou T. Computational Tools for Chemical Data Assimilation with CMAQ. [Internet] [Masters thesis]. Virginia Tech; 2010. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/31017.

Council of Science Editors:

Gou T. Computational Tools for Chemical Data Assimilation with CMAQ. [Masters Thesis]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/31017


Virginia Tech

5. Gao, Guangyue. A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii.

Degree: MS, Computer Science, 2016, Virginia Tech

 Toxoplasma gondii (T. gondii) is an intracellular protozoan parasite. The parasite can infect all warm-blooded vertebrates. Up to 30% of the world's human population carry… (more)

Subjects/Keywords: Gillespie Algorithm; Toxoplasma Gondii; Finite Difference Method; Transmission Dynamics; Compartment-Based Model; Stochastic Simulation

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APA (6th Edition):

Gao, G. (2016). A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78106

Chicago Manual of Style (16th Edition):

Gao, Guangyue. “A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii.” 2016. Masters Thesis, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/78106.

MLA Handbook (7th Edition):

Gao, Guangyue. “A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii.” 2016. Web. 29 Jan 2020.

Vancouver:

Gao G. A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii. [Internet] [Masters thesis]. Virginia Tech; 2016. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/78106.

Council of Science Editors:

Gao G. A Stochastic Model for The Transmission Dynamics of Toxoplasma Gondii. [Masters Thesis]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/78106


Virginia Tech

6. Martinez Arroyo, Gabriel Ernesto. Cu2cl: a Cuda-To-Opencl Translator for Multi- and Many-Core Architectures.

Degree: MS, Computer Science and Applications, 2011, Virginia Tech

 The use of graphics processing units (GPUs) in high-performance parallel computing continues to steadily become more prevalent, often as part of a heterogeneous system. For… (more)

Subjects/Keywords: GPU; Compilers; CUDA; OpenCL; Source Translation; Clang

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APA (6th Edition):

Martinez Arroyo, G. E. (2011). Cu2cl: a Cuda-To-Opencl Translator for Multi- and Many-Core Architectures. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/34233

Chicago Manual of Style (16th Edition):

Martinez Arroyo, Gabriel Ernesto. “Cu2cl: a Cuda-To-Opencl Translator for Multi- and Many-Core Architectures.” 2011. Masters Thesis, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/34233.

MLA Handbook (7th Edition):

Martinez Arroyo, Gabriel Ernesto. “Cu2cl: a Cuda-To-Opencl Translator for Multi- and Many-Core Architectures.” 2011. Web. 29 Jan 2020.

Vancouver:

Martinez Arroyo GE. Cu2cl: a Cuda-To-Opencl Translator for Multi- and Many-Core Architectures. [Internet] [Masters thesis]. Virginia Tech; 2011. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/34233.

Council of Science Editors:

Martinez Arroyo GE. Cu2cl: a Cuda-To-Opencl Translator for Multi- and Many-Core Architectures. [Masters Thesis]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/34233


Virginia Tech

7. Belgin, Mehmet. Structure-based Optimizations for Sparse Matrix-Vector Multiply.

Degree: PhD, Computer Science, 2010, Virginia Tech

 This dissertation introduces two novel techniques, OSF and PBR, to improve the performance of Sparse Matrix-vector Multiply (SMVM) kernels, which dominate the runtime of iterative… (more)

Subjects/Keywords: Code Generators; Vectorization; Sparse; SpMV; SMVM; Matrix Vector Multiply; PBR; OSF; thread pool; parallel SpMV

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APA (6th Edition):

Belgin, M. (2010). Structure-based Optimizations for Sparse Matrix-Vector Multiply. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/30260

Chicago Manual of Style (16th Edition):

Belgin, Mehmet. “Structure-based Optimizations for Sparse Matrix-Vector Multiply.” 2010. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/30260.

MLA Handbook (7th Edition):

Belgin, Mehmet. “Structure-based Optimizations for Sparse Matrix-Vector Multiply.” 2010. Web. 29 Jan 2020.

Vancouver:

Belgin M. Structure-based Optimizations for Sparse Matrix-Vector Multiply. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/30260.

Council of Science Editors:

Belgin M. Structure-based Optimizations for Sparse Matrix-Vector Multiply. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/30260


Virginia Tech

8. Tranquilli, Paul J. Lightly-Implicit Methods for the Time Integration of Large Applications.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Many scientific and engineering applications require the solution of large systems of initial value problems arising from method of lines discretization of partial differential equations.… (more)

Subjects/Keywords: Time Integration; Numerical PDEs; Numerical ODEs

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APA (6th Edition):

Tranquilli, P. J. (2016). Lightly-Implicit Methods for the Time Integration of Large Applications. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81974

Chicago Manual of Style (16th Edition):

Tranquilli, Paul J. “Lightly-Implicit Methods for the Time Integration of Large Applications.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/81974.

MLA Handbook (7th Edition):

Tranquilli, Paul J. “Lightly-Implicit Methods for the Time Integration of Large Applications.” 2016. Web. 29 Jan 2020.

Vancouver:

Tranquilli PJ. Lightly-Implicit Methods for the Time Integration of Large Applications. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/81974.

Council of Science Editors:

Tranquilli PJ. Lightly-Implicit Methods for the Time Integration of Large Applications. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/81974


Virginia Tech

9. Zavar Moosavi, Azam Sadat. Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification.

Degree: PhD, Computer Science, 2018, Virginia Tech

 Simulations and modeling of large-scale systems are vital to understanding real world phenomena. However, even advanced numerical models can only approximate the true physics. The… (more)

Subjects/Keywords: Uncertainty Quantification; Uncertainty Reduction; Stochastic Simulation of Chemical Reactions; Reduced-Order Models; Structural Uncertainty; Data Assimilation; Numerical Weather Prediction Models; Machine Learning

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APA (6th Edition):

Zavar Moosavi, A. S. (2018). Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/82491

Chicago Manual of Style (16th Edition):

Zavar Moosavi, Azam Sadat. “Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification.” 2018. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/82491.

MLA Handbook (7th Edition):

Zavar Moosavi, Azam Sadat. “Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification.” 2018. Web. 29 Jan 2020.

Vancouver:

Zavar Moosavi AS. Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/82491.

Council of Science Editors:

Zavar Moosavi AS. Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/82491


Virginia Tech

10. Corner, Sebastien Marc. Modeling, Sensitivity Analysis, and Optimization of Hybrid, Constrained Mechanical Systems.

Degree: PhD, Mechanical Engineering, 2018, Virginia Tech

 This dissertation provides a complete mathematical framework to compute the sensitivities with respect to system parameters for any second order hybrid Ordinary Differential Equation (ODE)… (more)

Subjects/Keywords: Sensitivity analysis; Hybrid systems; Constrained systems

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APA (6th Edition):

Corner, S. M. (2018). Modeling, Sensitivity Analysis, and Optimization of Hybrid, Constrained Mechanical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/82713

Chicago Manual of Style (16th Edition):

Corner, Sebastien Marc. “Modeling, Sensitivity Analysis, and Optimization of Hybrid, Constrained Mechanical Systems.” 2018. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/82713.

MLA Handbook (7th Edition):

Corner, Sebastien Marc. “Modeling, Sensitivity Analysis, and Optimization of Hybrid, Constrained Mechanical Systems.” 2018. Web. 29 Jan 2020.

Vancouver:

Corner SM. Modeling, Sensitivity Analysis, and Optimization of Hybrid, Constrained Mechanical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/82713.

Council of Science Editors:

Corner SM. Modeling, Sensitivity Analysis, and Optimization of Hybrid, Constrained Mechanical Systems. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/82713


Virginia Tech

11. Zhao, Junbo. A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control.

Degree: PhD, Electrical Engineering, 2018, Virginia Tech

 The enhancement of the reliability, security, and resiliency of electric power systems depends on the availability of fast, accurate, and robust dynamic state estimators. These… (more)

Subjects/Keywords: Kalman filter; Robust statistics; Power system state estimation; Dynamic state estimation; Unscented transformation; Robust control theory; Estimation theory; Power system dynamics and control; Outliers; Cyber attacks; Phasor measurement units

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APA (6th Edition):

Zhao, J. (2018). A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/83423

Chicago Manual of Style (16th Edition):

Zhao, Junbo. “A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control.” 2018. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/83423.

MLA Handbook (7th Edition):

Zhao, Junbo. “A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control.” 2018. Web. 29 Jan 2020.

Vancouver:

Zhao J. A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/83423.

Council of Science Editors:

Zhao J. A Robust Dynamic State and Parameter Estimation Framework for Smart Grid Monitoring and Control. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/83423


Virginia Tech

12. Aguilar Huacan, Boris Abner. Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling.

Degree: PhD, Computer Science, 2014, Virginia Tech

 Biomolecular Modeling is playing an important role in many practical applications such as biotechnology and structure-based drug design. One of the essential requirements of Biomolecular… (more)

Subjects/Keywords: Molecular Modeling; Implicit solvents; Generalized Born Model

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APA (6th Edition):

Aguilar Huacan, B. A. (2014). Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64409

Chicago Manual of Style (16th Edition):

Aguilar Huacan, Boris Abner. “Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/64409.

MLA Handbook (7th Edition):

Aguilar Huacan, Boris Abner. “Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling.” 2014. Web. 29 Jan 2020.

Vancouver:

Aguilar Huacan BA. Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/64409.

Council of Science Editors:

Aguilar Huacan BA. Improving of the accuracy and efficiency of implicit solvent models in Biomolecular Modeling. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/64409


Virginia Tech

13. Nino Ruiz, Elias David. Efficient formulation and implementation of ensemble based methods in data assimilation.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Ensemble-based methods have gained widespread popularity in the field of data assimilation. An ensemble of model realizations encapsulates information about the error correlations driven by… (more)

Subjects/Keywords: Ensemble-based methods; ensemble Kalman filter; ensemble square root filter; hybrid data assimilation; background error covariance matrix estimation; parallel data assimilation

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APA (6th Edition):

Nino Ruiz, E. D. (2016). Efficient formulation and implementation of ensemble based methods in data assimilation. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64438

Chicago Manual of Style (16th Edition):

Nino Ruiz, Elias David. “Efficient formulation and implementation of ensemble based methods in data assimilation.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/64438.

MLA Handbook (7th Edition):

Nino Ruiz, Elias David. “Efficient formulation and implementation of ensemble based methods in data assimilation.” 2016. Web. 29 Jan 2020.

Vancouver:

Nino Ruiz ED. Efficient formulation and implementation of ensemble based methods in data assimilation. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/64438.

Council of Science Editors:

Nino Ruiz ED. Efficient formulation and implementation of ensemble based methods in data assimilation. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/64438


Virginia Tech

14. Linford, John Christian. Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies.

Degree: PhD, Computer Science, 2010, Virginia Tech

 The new generations of multi-core chipset architectures achieve unprecedented levels of computational power while respecting physical and economical constraints. The cost of this power is… (more)

Subjects/Keywords: hardware; high performance computing; software

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APA (6th Edition):

Linford, J. C. (2010). Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/27599

Chicago Manual of Style (16th Edition):

Linford, John Christian. “Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies.” 2010. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/27599.

MLA Handbook (7th Edition):

Linford, John Christian. “Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies.” 2010. Web. 29 Jan 2020.

Vancouver:

Linford JC. Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/27599.

Council of Science Editors:

Linford JC. Accelerating Atmospheric Modeling Through Emerging Multi-core Technologies. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/27599


Virginia Tech

15. Hays, Joseph T. Parametric Optimal Design Of Uncertain Dynamical Systems.

Degree: PhD, Mechanical Engineering, 2011, Virginia Tech

 This research effort develops a comprehensive computational framework to support the parametric optimal design of uncertain dynamical systems. Uncertainty comes from various sources, such as:… (more)

Subjects/Keywords: Ordinary Differential Equations (ODEs); Trajectory Planning; Motion Planning; Generalized Polynomial Chaos (gPC); Uncertainty Quantification; Multi-Objective Optimization (MOO); Nonlinear Programming (NLP); Dynamic Optimization; Optimal Control; Robust Design Optimization (RDO); Collocation; Uncertainty Apportionment; Tolerance Allocation; Multibody Dynamics; Differential Algebraic Equations (DAEs)

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APA (6th Edition):

Hays, J. T. (2011). Parametric Optimal Design Of Uncertain Dynamical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/28850

Chicago Manual of Style (16th Edition):

Hays, Joseph T. “Parametric Optimal Design Of Uncertain Dynamical Systems.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/28850.

MLA Handbook (7th Edition):

Hays, Joseph T. “Parametric Optimal Design Of Uncertain Dynamical Systems.” 2011. Web. 29 Jan 2020.

Vancouver:

Hays JT. Parametric Optimal Design Of Uncertain Dynamical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/28850.

Council of Science Editors:

Hays JT. Parametric Optimal Design Of Uncertain Dynamical Systems. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/28850


Virginia Tech

16. Lloyd, John William. Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach.

Degree: PhD, Mechanical Engineering, 2011, Virginia Tech

 A method to adapt the Generalized Predictive Control parameters to improve broadband disturbance rejection was developed and tested. The effect of the parameters on disturbance… (more)

Subjects/Keywords: GPC; generalized predictive control; active control; adaptive control; fuzzy logic; fuzzy logic adaptation; vibration control; disturbance rejection

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APA (6th Edition):

Lloyd, J. W. (2011). Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/29306

Chicago Manual of Style (16th Edition):

Lloyd, John William. “Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/29306.

MLA Handbook (7th Edition):

Lloyd, John William. “Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach.” 2011. Web. 29 Jan 2020.

Vancouver:

Lloyd JW. Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/29306.

Council of Science Editors:

Lloyd JW. Generalized Predictive Control Parameter Adaptation Using a Fuzzy Logic Approach. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/29306


Virginia Tech

17. Ahn, Tae-Hyuk. Computational Techniques for the Analysis of Large Scale Biological Systems.

Degree: PhD, Computer Science, 2016, Virginia Tech

 An accelerated pace of discovery in biological sciences is made possible by a new generation of computational biology and bioinformatics tools. In this dissertation we… (more)

Subjects/Keywords: Stochastic simulation algorithm (SSA); Parallel load balancing; Cell cycle; RNA-Sequencing; Stochastic differential equations (SDEs)

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APA (6th Edition):

Ahn, T. (2016). Computational Techniques for the Analysis of Large Scale Biological Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/77162

Chicago Manual of Style (16th Edition):

Ahn, Tae-Hyuk. “Computational Techniques for the Analysis of Large Scale Biological Systems.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/77162.

MLA Handbook (7th Edition):

Ahn, Tae-Hyuk. “Computational Techniques for the Analysis of Large Scale Biological Systems.” 2016. Web. 29 Jan 2020.

Vancouver:

Ahn T. Computational Techniques for the Analysis of Large Scale Biological Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/77162.

Council of Science Editors:

Ahn T. Computational Techniques for the Analysis of Large Scale Biological Systems. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/77162


Virginia Tech

18. Liu, Zhen. Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features.

Degree: PhD, Computer Science, 2012, Virginia Tech

 In this thesis we study stochastic modeling and simulation methods for biochemical systems. The thesis is focused on systems with multi-state and multi-scale features and… (more)

Subjects/Keywords: SSA; Stochsim; rule-based modeling; QSSA; hybrid method

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APA (6th Edition):

Liu, Z. (2012). Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/19191

Chicago Manual of Style (16th Edition):

Liu, Zhen. “Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features.” 2012. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/19191.

MLA Handbook (7th Edition):

Liu, Zhen. “Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features.” 2012. Web. 29 Jan 2020.

Vancouver:

Liu Z. Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/19191.

Council of Science Editors:

Liu Z. Stochastic Simulation Methods for Biochemical Systems with Multi-state and Multi-scale Features. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/19191


Virginia Tech

19. Zhu, Yitao. Sensitivity Analysis and Optimization of Multibody Systems.

Degree: PhD, Mechanical Engineering, 2015, Virginia Tech

 Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of… (more)

Subjects/Keywords: Sensitivity Analysis; Optimization; Multibody Dynamics; Vehicle Dynamics

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APA (6th Edition):

Zhu, Y. (2015). Sensitivity Analysis and Optimization of Multibody Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/71649

Chicago Manual of Style (16th Edition):

Zhu, Yitao. “Sensitivity Analysis and Optimization of Multibody Systems.” 2015. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/71649.

MLA Handbook (7th Edition):

Zhu, Yitao. “Sensitivity Analysis and Optimization of Multibody Systems.” 2015. Web. 29 Jan 2020.

Vancouver:

Zhu Y. Sensitivity Analysis and Optimization of Multibody Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/71649.

Council of Science Editors:

Zhu Y. Sensitivity Analysis and Optimization of Multibody Systems. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/71649


Virginia Tech

20. Hebbur Venkata Subba Rao, Vishwas. Adjoint based solution and uncertainty quantification techniques for variational inverse problems.

Degree: PhD, Computer Science, 2015, Virginia Tech

 Variational inverse problems integrate computational simulations of physical phenomena with physical measurements in an informational feedback control system. Control parameters of the computational model are… (more)

Subjects/Keywords: Data assimilation; Inverse problems; sensitivity analysis

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APA (6th Edition):

Hebbur Venkata Subba Rao, V. (2015). Adjoint based solution and uncertainty quantification techniques for variational inverse problems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/76665

Chicago Manual of Style (16th Edition):

Hebbur Venkata Subba Rao, Vishwas. “Adjoint based solution and uncertainty quantification techniques for variational inverse problems.” 2015. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/76665.

MLA Handbook (7th Edition):

Hebbur Venkata Subba Rao, Vishwas. “Adjoint based solution and uncertainty quantification techniques for variational inverse problems.” 2015. Web. 29 Jan 2020.

Vancouver:

Hebbur Venkata Subba Rao V. Adjoint based solution and uncertainty quantification techniques for variational inverse problems. [Internet] [Doctoral dissertation]. Virginia Tech; 2015. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/76665.

Council of Science Editors:

Hebbur Venkata Subba Rao V. Adjoint based solution and uncertainty quantification techniques for variational inverse problems. [Doctoral Dissertation]. Virginia Tech; 2015. Available from: http://hdl.handle.net/10919/76665


Virginia Tech

21. Blanchard, Emmanuel Dominique. Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters.

Degree: PhD, Mechanical Engineering, 2010, Virginia Tech

 Mechanical systems operate under parametric and external excitation uncertainties. The polynomial chaos approach has been shown to be more efficient than Monte Carlo approaches for… (more)

Subjects/Keywords: Collocation; Polynomial Chaos; Parametric Uncertainty; Parameter Estimation; Extended Kalman Filter (EKF); Bayesian Estimation; Vehicle Dynamics; Control Design; Robust Control; LQR

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APA (6th Edition):

Blanchard, E. D. (2010). Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/26727

Chicago Manual of Style (16th Edition):

Blanchard, Emmanuel Dominique. “Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters.” 2010. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/26727.

MLA Handbook (7th Edition):

Blanchard, Emmanuel Dominique. “Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters.” 2010. Web. 29 Jan 2020.

Vancouver:

Blanchard ED. Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/26727.

Council of Science Editors:

Blanchard ED. Polynomial Chaos Approaches to Parameter Estimation and Control Design for Mechanical Systems with Uncertain Parameters. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/26727


Virginia Tech

22. Cioaca, Alexandru George. A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation.

Degree: PhD, Computer Science, 2013, Virginia Tech

 A deep scientific understanding of complex physical systems, such as the atmosphere, can be achieved neither by direct measurements nor by numerical simulations alone. Data… (more)

Subjects/Keywords: data assimilation; dynamic data-driven problem; second-order adjoints; adaptive observations; sensor placement; intelligent sensors; sensitivity analysis; uncertainty quantification; nonlinear optimization; inverse problems; parameter estimation; matrix-free linear solvers; truncated singular value decomposition

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APA (6th Edition):

Cioaca, A. G. (2013). A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/51795

Chicago Manual of Style (16th Edition):

Cioaca, Alexandru George. “A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation.” 2013. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/51795.

MLA Handbook (7th Edition):

Cioaca, Alexandru George. “A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation.” 2013. Web. 29 Jan 2020.

Vancouver:

Cioaca AG. A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation. [Internet] [Doctoral dissertation]. Virginia Tech; 2013. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/51795.

Council of Science Editors:

Cioaca AG. A Computational Framework for Assessing and Optimizing the Performance of Observational Networks in 4D-Var Data Assimilation. [Doctoral Dissertation]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/51795


Virginia Tech

23. Alexe, Mihai. Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems.

Degree: PhD, Computer Science, 2011, Virginia Tech

 Adaptivity in both space and time has become the norm for solving problems modeled by partial differential equations. The size of the discretized problem makes… (more)

Subjects/Keywords: Inverse problems; Adjoint Method; Adaptive Mesh Refinement; Automatic Differentiation

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APA (6th Edition):

Alexe, M. (2011). Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/37515

Chicago Manual of Style (16th Edition):

Alexe, Mihai. “Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/37515.

MLA Handbook (7th Edition):

Alexe, Mihai. “Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems.” 2011. Web. 29 Jan 2020.

Vancouver:

Alexe M. Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/37515.

Council of Science Editors:

Alexe M. Adjoint-based space-time adaptive solution algorithms for sensitivity analysis and inverse problems. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/37515


Virginia Tech

24. Umsrithong, Anake. Deterministic and Stochastic Semi-Empirical Transient Tire Models.

Degree: PhD, Mechanical Engineering, 2012, Virginia Tech

 The tire is one of the most important components of the vehicle. It has many functions, such as supporting the load of the vehicle, transmitting… (more)

Subjects/Keywords: tire model; uncertainties; vehicle dynamics

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APA (6th Edition):

Umsrithong, A. (2012). Deterministic and Stochastic Semi-Empirical Transient Tire Models. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/26270

Chicago Manual of Style (16th Edition):

Umsrithong, Anake. “Deterministic and Stochastic Semi-Empirical Transient Tire Models.” 2012. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/26270.

MLA Handbook (7th Edition):

Umsrithong, Anake. “Deterministic and Stochastic Semi-Empirical Transient Tire Models.” 2012. Web. 29 Jan 2020.

Vancouver:

Umsrithong A. Deterministic and Stochastic Semi-Empirical Transient Tire Models. [Internet] [Doctoral dissertation]. Virginia Tech; 2012. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/26270.

Council of Science Editors:

Umsrithong A. Deterministic and Stochastic Semi-Empirical Transient Tire Models. [Doctoral Dissertation]. Virginia Tech; 2012. Available from: http://hdl.handle.net/10919/26270


Virginia Tech

25. Anandakrishnan, Ramamoorthi. Speeding up electrostatic computations for molecular dynamics.

Degree: PhD, Computer Science and Applications, 2011, Virginia Tech

 Molecular dynamics (MD) simulations are routinely used to study the structure and function of biological molecules. However the accuracy and duration of these simulations are… (more)

Subjects/Keywords: statistical mechanics; biomolecular electrostatics; molecular dynamics

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APA (6th Edition):

Anandakrishnan, R. (2011). Speeding up electrostatic computations for molecular dynamics. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/40262

Chicago Manual of Style (16th Edition):

Anandakrishnan, Ramamoorthi. “Speeding up electrostatic computations for molecular dynamics.” 2011. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/40262.

MLA Handbook (7th Edition):

Anandakrishnan, Ramamoorthi. “Speeding up electrostatic computations for molecular dynamics.” 2011. Web. 29 Jan 2020.

Vancouver:

Anandakrishnan R. Speeding up electrostatic computations for molecular dynamics. [Internet] [Doctoral dissertation]. Virginia Tech; 2011. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/40262.

Council of Science Editors:

Anandakrishnan R. Speeding up electrostatic computations for molecular dynamics. [Doctoral Dissertation]. Virginia Tech; 2011. Available from: http://hdl.handle.net/10919/40262


Virginia Tech

26. Chen, Minghan. Stochastic Modeling and Simulation of Multiscale Biochemical Systems.

Degree: PhD, Computer Science and Applications, 2019, Virginia Tech

 Modeling and simulation of biochemical networks faces numerous challenges as biochemical networks are discovered with increased complexity and unknown mechanisms. With improvement in experimental techniques,… (more)

Subjects/Keywords: Caulobacter cell cycle model; hybrid stochastic simulation algorithm; stochastic parameter optimization

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APA (6th Edition):

Chen, M. (2019). Stochastic Modeling and Simulation of Multiscale Biochemical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90898

Chicago Manual of Style (16th Edition):

Chen, Minghan. “Stochastic Modeling and Simulation of Multiscale Biochemical Systems.” 2019. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/90898.

MLA Handbook (7th Edition):

Chen, Minghan. “Stochastic Modeling and Simulation of Multiscale Biochemical Systems.” 2019. Web. 29 Jan 2020.

Vancouver:

Chen M. Stochastic Modeling and Simulation of Multiscale Biochemical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/90898.

Council of Science Editors:

Chen M. Stochastic Modeling and Simulation of Multiscale Biochemical Systems. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90898

27. Attia, Ahmed Mohamed Mohamed. Advanced Sampling Methods for Solving Large-Scale Inverse Problems.

Degree: PhD, Computer Science and Applications, 2016, Virginia Tech

 Ensemble and variational techniques have gained wide popularity as the two main approaches for solving data assimilation and inverse problems. The majority of the methods… (more)

Subjects/Keywords: Data Assimilation; Inverse Problems; Uncertainty Quantification; Hamiltonian Monte-Carlo; Cluster Sampling Filters; High Performance Computing.

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APA (6th Edition):

Attia, A. M. M. (2016). Advanced Sampling Methods for Solving Large-Scale Inverse Problems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/73683

Chicago Manual of Style (16th Edition):

Attia, Ahmed Mohamed Mohamed. “Advanced Sampling Methods for Solving Large-Scale Inverse Problems.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/73683.

MLA Handbook (7th Edition):

Attia, Ahmed Mohamed Mohamed. “Advanced Sampling Methods for Solving Large-Scale Inverse Problems.” 2016. Web. 29 Jan 2020.

Vancouver:

Attia AMM. Advanced Sampling Methods for Solving Large-Scale Inverse Problems. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/73683.

Council of Science Editors:

Attia AMM. Advanced Sampling Methods for Solving Large-Scale Inverse Problems. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/73683


Virginia Tech

28. Zhang, Hong. Efficient Time Stepping Methods and Sensitivity Analysis for Large Scale Systems of Differential Equations.

Degree: PhD, Computer Science, 2014, Virginia Tech

 Many fields in science and engineering require large-scale numerical simulations of complex systems described by differential equations. These systems are typically multi-physics (they are driven… (more)

Subjects/Keywords: Time Stepping; General Linear Methods; Implicit-explicit; Sensitivity Analysis

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APA (6th Edition):

Zhang, H. (2014). Efficient Time Stepping Methods and Sensitivity Analysis for Large Scale Systems of Differential Equations. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/50492

Chicago Manual of Style (16th Edition):

Zhang, Hong. “Efficient Time Stepping Methods and Sensitivity Analysis for Large Scale Systems of Differential Equations.” 2014. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/50492.

MLA Handbook (7th Edition):

Zhang, Hong. “Efficient Time Stepping Methods and Sensitivity Analysis for Large Scale Systems of Differential Equations.” 2014. Web. 29 Jan 2020.

Vancouver:

Zhang H. Efficient Time Stepping Methods and Sensitivity Analysis for Large Scale Systems of Differential Equations. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/50492.

Council of Science Editors:

Zhang H. Efficient Time Stepping Methods and Sensitivity Analysis for Large Scale Systems of Differential Equations. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/50492


Virginia Tech

29. Singh, Kumaresh. Efficient Computational Tools for Variational Data Assimilation and Information Content Estimation.

Degree: PhD, Computer Science, 2010, Virginia Tech

 The overall goals of this dissertation are to advance the field of chemical data assimilation, and to develop efficient computational tools that allow the atmospheric… (more)

Subjects/Keywords: Information Theory; Chemical Transport Models; Global Ozone Measurements; Model Adjoint Construction; Adjoint Sensitivity Analysis; Error Covariance Matrices; Data Assimilation

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APA (6th Edition):

Singh, K. (2010). Efficient Computational Tools for Variational Data Assimilation and Information Content Estimation. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/39125

Chicago Manual of Style (16th Edition):

Singh, Kumaresh. “Efficient Computational Tools for Variational Data Assimilation and Information Content Estimation.” 2010. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/39125.

MLA Handbook (7th Edition):

Singh, Kumaresh. “Efficient Computational Tools for Variational Data Assimilation and Information Content Estimation.” 2010. Web. 29 Jan 2020.

Vancouver:

Singh K. Efficient Computational Tools for Variational Data Assimilation and Information Content Estimation. [Internet] [Doctoral dissertation]. Virginia Tech; 2010. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/39125.

Council of Science Editors:

Singh K. Efficient Computational Tools for Variational Data Assimilation and Information Content Estimation. [Doctoral Dissertation]. Virginia Tech; 2010. Available from: http://hdl.handle.net/10919/39125

30. Li, Fei. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems.

Degree: PhD, Computer Science, 2016, Virginia Tech

 Reaction Diffusion Master Equation (RDME) framework, characterized by the discretization of the spatial domain, is one of the most widely used methods in the stochastic… (more)

Subjects/Keywords: stochastic simulation; reaction-diffusion systems; Caulobacter crescentus

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APA (6th Edition):

Li, F. (2016). Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/64913

Chicago Manual of Style (16th Edition):

Li, Fei. “Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems.” 2016. Doctoral Dissertation, Virginia Tech. Accessed January 29, 2020. http://hdl.handle.net/10919/64913.

MLA Handbook (7th Edition):

Li, Fei. “Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems.” 2016. Web. 29 Jan 2020.

Vancouver:

Li F. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems. [Internet] [Doctoral dissertation]. Virginia Tech; 2016. [cited 2020 Jan 29]. Available from: http://hdl.handle.net/10919/64913.

Council of Science Editors:

Li F. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems. [Doctoral Dissertation]. Virginia Tech; 2016. Available from: http://hdl.handle.net/10919/64913

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