Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

PROBLEM FORMULATION FOR MULTIDISCIPLINARY OPTIMIZATION

This paper is about multidisciplinary (design) optimization, or MDO, the coupling of two or more analysis disciplines with numerical optimization. The paper has three goals. First, it is an expository introduction to MDO aimed at those who do research on optimization algorithms, since the optimization community has much to contribute to this important class of computational engineering problems. Second, this paper presents to the MDO research community a new abstraction for multidisciplinary analysis and design problems as well as new decomposition formulations for these problems. Third, the "individual discipline feasible " (IDF) approaches introduced here make use of existing specialized analysis codes, and they introduce significant opportunities for coarse-grained computational parallelism particularly well suited to heterogeneous computing environments. The key distinguishing characteristic of the three fundamental approaches to MDO formulation discussed here is the kind of disciplinary feasibility that must be maintained at each optimization iteration. Other formulation issues, such as the sensitivities required, are also considered. This discussion highlights the trade-offs between reuse of existing software, computational requirements, and probability of success

Overview of Alzheimer's and Parkinson's diseases and the role of protein aggregation in these neurodegenerative diseases

Alzheimer’s disease (AD) and Parkinson’s disease (PD) are the two most common neurodegenerative diseases, and together they affect approximately 56 million people worldwide. Despite distinct clinical features and the involvement of relatively disparate neural systems, both diseases are proteinopathies with significant overlap in some of the potential mechanisms of neuronal dysfunction and death. Models of protein aggregation and the relationship to disease onset and progression have widespread implications. Clearly, understanding the role protein aggregation plays in supporting or damaging cellular function is key to understanding the mechanisms of disease and developing new, effective therapies. A second application of this knowledge is to the development of relevant biomarkers of disease. Together, early identification of disease onset and the targeting of appropriate cellular pathways or polypeptides provide the best opportunity to develop disease-modifying interventions for people with neurodegenerative diseases. This chapter provides an overview of AD and PD, and it discusses current theories of the relationship between protein aggregation and neurodegeneration