Regularized Adaptive Methods for Classes of Nonlinear Partial Differential Equations

非线性扩散方程经常出现在热传导、地下水流动和多孔介质流动等物理过程的模拟中。这样的方程出现在描述地下污染物分布的环境相关建模问题中。在这些非线性问题中，扩散系数取决于解和可能的坐标;因此，模型的数值解不能通过直接方法确定。一般来说，只有通过解决一系列更简单的近似问题，并对解决方案进行连续改进，才能找到解决方案。然而，如果以标准方式进行，则该方案的收敛可能失败，并且当前的数值模拟可能受到这些标准迭代技术失败的限制。本研究的重点是发展稳定、收敛的数值迭代算法，以有效地求解非线性扩散方程，解决现实物理建模中的科学计算问题，其技术目标是为各类非线性扩散方程开发高效、鲁棒的模拟技术。非线性扩散问题的有限元解在渐近状态下具有良好的逼近性质，但计算这些离散解的合理方法尚未开发。 将在自适应有限元方法的框架内开发正则化自适应方法，其中（1）迭代收敛于离散解;以及（2）离散解收敛于偏微分方程的解，因为网格被选择性地细化。 这项工作的目的之一是制定指导原则的正规化的诱导离散问题与误差指标，以确定网格细化。 正则化和误差指标的组合应该允许计算离散解，并从理论角度保证收敛到正确的解。 将为模型问题的代表性类别开发由健全的数学理论支持的计算方法，并且所开发的方法将扩展到更大的、计算要求高的模拟，例如，对注入地球地下的CO2的分布进行建模，作为长期储存的潜在手段。预计在该项目过程中取得的技术进步将推动实现高效准确的数值模拟工具，使实际建模的问题与现实的物理属性。

Nonlinear diffusion equations appear often in simulations of physical processes such as heat conduction, groundwater flow, and flow in porous media. Such equations appear in environmentally relevant modeling problems describing the distribution of subsurface contaminants. In these nonlinear problems, the diffusion coefficient is dependent on the solution and possibly its coordinates; as such, a numerical solution to the model cannot be determined by direct means. Generally, solutions can only be found by solving a sequence of simpler approximate problems, and making successive improvements to the solution. Convergence of the scheme may fail however if this is carried out in a standard way, and current numerical simulations can be limited by the failure of these standard iterative techniques. The focus of this work is on the mathematically rigorous development of stable and convergent iterative numerical algorithms to efficiently solve nonlinear diffusion equations, addressing a substantial problem in scientific computing for realistic physical modeling.The technical goal of this project is to develop efficient and robust simulation technology for classes of nonlinear diffusion equations. Finite element solutions for nonlinear diffusion problems are known to have good approximation properties in the asymptotic regime, but a sound methodology to compute those discrete solutions has yet to be developed. Regularized adaptive methods will be developed within the framework of adaptive finite element methods, for which (1) the iterates converge to discrete solutions; and (2) the discrete solutions converge to the solution of the partial differential equation, as the mesh is selectively refined. One of the aims of this work is to develop guiding principles for the regularization of the induced discrete problems in concert with error indicators to determine the mesh refinement. The combination of the regularization and error indicators should both allow the computation of a discrete solution, and guarantee the convergence to a correct solution from a theoretical standpoint. Computational methods backed up by sound mathematical theory will be developed for representative classes of model problems, and the developed methods will be extended to larger, computationally-demanding simulations, for example to model the distribution of C02 injected into the earth's subsurface as a potential means for long-term storage. It is expected that the technical advances made in the course of this project will advance the realization of efficient and accurate numerical simulation tools that allow the practical modeling of problems with realistic physical attributes.