Mathematical models in systems biology often have many parameters, such as biochemical reaction rates, whose true values are unknown. When the number of parameters is large, it becomes computationally difficult to analyze their effects and to estimate parameter values from experimental data. This is especially challenging when the model is expen...
Creator:
Chou, Ching-Shan (The Ohio State University)
Created:
2018-05-29
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Many science and engineering problems lead to optimization problems governed by partial differential equations (PDEs), and in many of these problems some of the problem data are not known exactly. I focus on a class of such optimization problems where the uncertain data are modeled by random variables or random fields, and where decision variabl...
Creator:
Heinkenschloss, Matthias (Rice University)
Created:
2016-03-14
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Lead-acid batteries are capable of providing large amounts of power for short durations. Unlike Lithium-Ion batteries, they age well when stored at (near) full capacity, and are relatively inexpensive in terms of storage capacity. However, the number of cycles a lead-acid battery can endure before end of life is significantly lower than that of ...
Creator:
Lee, Ben (Siemens)
Created:
2016-02-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In 1993, The Goodyear Tire & Rubber Company ventured into an improbable, close-working partnership with Sandia National Laboratories, a U.S. nuclear weapons lab. We will discuss, from the industry perspective, the lessons learned while initiating, managing, and gaining value from the relationship, including pitfalls and how to surmount them, in ...
Creator:
Miller, Loren (DataMetric Innovations LLC)
Created:
2016-02-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Uncertainty and error are ubiquitous in predictive modeling and simulation due to unknown model parameters, boundary conditions and various sources of numerical error. Consequently, there is considerable interest in developing efficient and accurate methods to quantify the uncertainty in the outputs of a computational model. Monte Carlo techniqu...
Creator:
Wildey, Timothy (Sandia National Laboratories)
Created:
2016-02-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.