The progress of science and engineering in the U.S. is now limited by the lack of computational infrastructure to incorporate uncertain information from experimental data into complex predictions made by computer simulation. For example, uncertainty in chemical models directly impacts the design of efficient combustion systems at the core of the U.S. energy and transportation infrastructure. The ability to incorporate uncertainty into computational models will directly advance understanding of how this uncertainty impacts subsequent model predictions and, therefore, our ability to make reliable decisions based on the predictions of these complex models. This work aims to provide such a computational infrastructure that can easily leverage the large scale computing resources deployed in the U.S. The framework developed in this work can enable improvements in the development of computational models by providing methodologies and tools for ascertaining the large sources of uncertainty. Additionally, the framework aims to enable better design of physical experiments by facilitating the identification of experimental scenarios that best inform the computational model. The cyberinfrastructure developed in this work is also aimed to provide the foundation for training the next generation of scientists and engineers to use contemporary computational and data-enabled science tools, theory, and practice. The project, thus is aligned to NSF's mission to promote the progress of science and to advance the national health, prosperity and welfare.
The digital environment that this work aims to develop will support the Bayesian inference of unknown parameters in mathematical models based on partial differential equations (PDEs). Specifically, the computational environment will facilitate the creation, experimentation, and examination of all aspects of computational predictions with uncertainty: mathematical models, finite element formulations of PDEs, statistical surrogate models for complex systems, and algorithms for solving the statistical inverse problem. Significant progress in the state-of-the-art of both the solution of statistical inverse problems and the design of physical experiments will be made by easily enabling the presence and examination of uncertainty within complex simulation models. This platform will use modern high performance computing algorithms and be portable to extreme-scale computing infrastructure for the solution of those models. Finally, all elements will be deployed in portable, efficient, and easy-to-use software elements for use by the community at large.
