2013 - SIAM/ACM Prize in Computational Science and Engineering For her pioneering research in methods for the computational solution of differential-algebraic equations, their incorporation into widely distributed software and scientific applications, and her significant accomplishments in pioneering computational science and engineering education.
2011 - ACM Fellow For contributions to computational science.
2009 - SIAM Fellow For contributions to numerical ordinary differential equations and differential-algebraic equations and computational science.
2007 - Fellow of the American Society of Mechanical Engineers
2005 - Fellow of the American Association for the Advancement of Science (AAAS)
2004 - Member of the National Academy of Engineering For advances in the numerical solution of differential/algebraic equations and their incorporation into widely distributed software.
Linda R. Petzold mainly focuses on Differential equation, Stochastic simulation, Stochastic process, Differential algebraic equation and Applied mathematics. Her Differential equation study combines topics in areas such as Differential, Numerical analysis, Algebraic equation and Ode. Her Numerical analysis research is multidisciplinary, incorporating perspectives in Initial value problem and Nonlinear system.
Her Stochastic simulation research is multidisciplinary, incorporating elements of Stochastic modelling, Poisson distribution, Stochastic optimization, Mathematical optimization and Calculus. Her work deals with themes such as Statistical physics and Selection, which intersect with Stochastic process. Differential algebraic equation is a primary field of her research addressed under Mathematical analysis.
Linda R. Petzold mostly deals with Applied mathematics, Stochastic simulation, Mathematical analysis, Differential algebraic equation and Mathematical optimization. Her Applied mathematics research includes elements of Partial differential equation, Calculus and Reduced order. Her Stochastic simulation research includes themes of Stochastic process, Software and Algorithm.
Linda R. Petzold works mostly in the field of Stochastic process, limiting it down to concerns involving Statistical physics and, occasionally, Reaction–diffusion system. Her study on Differential algebraic equation is covered under Differential equation. Linda R. Petzold combines subjects such as Numerical analysis, Algebraic equation and Ode with her study of Differential equation.
Artificial intelligence, Machine learning, Deep learning, Mating projection and Biophysics are her primary areas of study. The various areas that Linda R. Petzold examines in her Artificial intelligence study include Chronic pain, Disease and Pattern recognition. Her Machine learning research incorporates themes from Multiscale modeling, Field and Systems biology.
Her Multiscale modeling research integrates issues from Complement, State, Biomedicine, System dynamics and Computational mechanics. Her biological study spans a wide range of topics, including Deep space exploration and Biofeedback, Physical therapy, Pain reduction. Her study in Biophysics is interdisciplinary in nature, drawing from both Cell, Budding yeast and Morphogenesis.
Linda R. Petzold spends much of her time researching Bayesian inference, Cell biology, Machine learning, Artificial intelligence and Master equation. Her Bayesian inference study integrates concerns from other disciplines, such as Mathematical analysis and Stiefel manifold. In general Machine learning study, her work on Semi-supervised learning often relates to the realm of Function, thereby connecting several areas of interest.
She integrates several fields in her works, including Master equation, Reaction–diffusion system, Complex geometry, Discretization, Diffusion equation and Applied mathematics. Her studies in Mathematical optimization integrate themes in fields like Orthogonal matrix and Inference. The concepts of her Inference study are interwoven with issues in Algorithm and Gibbs sampling.
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Numerical solution of initial-value problems in differential-algebraic equations
Kathryn Eleda Brenan;S. L. Campbell;Linda Ruth Petzold.
Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations
Uri M. Ascher;Linda R. Petzold.
Description of DASSL: a differential/algebraic system solver
10. international mathematics and computers simulation congress on systems simulation and scientific computation, Montreal, Canada, 9 Aug 1982 (1982)
Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations
Siam Journal on Scientific and Statistical Computing (1983)
Efficient step size selection for the tau-leaping simulation method
Yang Cao;Daniel T. Gillespie;Linda R. Petzold.
Journal of Chemical Physics (2006)
Differential/Algebraic Equations are not ODE's
Siam Journal on Scientific and Statistical Computing (1982)
ODE METHODS FOR THE SOLUTION OF DIFFERENTIAL/ALGEBRAIC SYSTEMS
C. W. Gear;L. R. Petzold.
SIAM Journal on Numerical Analysis (1984)
The Slow-Scale Stochastic Simulation Algorithm
Yang Cao;Daniel T. Gillespie;Linda R. Petzold.
Journal of Chemical Physics (2005)
Using Krylov methods in the solution of large-scale differential-algebraic systems
Peter N. Brown;Alan C. Hindmarsh;Linda R. Petzold.
SIAM Journal on Scientific Computing (1994)
Stiffness in stochastic chemically reacting systems: The implicit tau-leaping method
Muruhan Rathinam;Linda R. Petzold;Yang Cao;Daniel T. Gillespie.
Journal of Chemical Physics (2003)
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