An efficient regularization method for a large scale ill-posed geothermal problem
نویسندگان
چکیده
منابع مشابه
Projected Tikhonov Regularization of Large-Scale Discrete Ill-Posed Problems
The solution of linear discrete ill-posed problems is very sensitive to perturbations in the data. Confidence intervals for solution coordinates provide insight into the sensitivity. This paper presents an efficient method for computing confidence intervals for large-scale linear discrete ill-posed problems. The method is based on approximating the matrix in these problems by a partial singular...
متن کاملA Finite Element Method for an Ill-Posed Problem
For an ill-posed problem which has its origin in several applications (e.g. electro-cardiology) a weak formulation is given over a Hilbert space without any constraints. This is achieved by means of Lagrangian multipliers. Beside theoretical questions (e.g. existence and uniqueness of a solutuion) a nite element approximation is considered. Error estimates, an investigation of the condition num...
متن کاملA Numerical Method for an Ill-posed Problem
The noncharacterisitic initial value problem for the one-dimensional heat equation (the solution and its rst-order spatial derivative speciied on an interval of the time axis) is well known to be ill-posed. Nevertheless, the author has proved in 4] that nonnegative solutions of this problem depend continuously on the initial data. However, this result does not solve the problem of constructing ...
متن کاملA regularization method for ill-posed bilevel optimization problems
We present a regularization method to approach a solution of the pessimistic formulation of ill -posed bilevel problems . This allows to overcome the difficulty arising from the non uniqueness of the lower level problems solutions and responses. We prove existence of approximated solutions, give convergence result using Hoffman-like assumptions. We end with objective value error estimates.
متن کاملOptimal control as a regularization method for ill-posed problems
We describe two regularization techniques based on optimal control for solving two types of ill-posed problems. We include convergence proofs of the regularization method and error estimates. We illustrate our method through problems in signal processing and parameter identification using an efficient Riccati solver. Our numerical results are compared to the same examples solved using Tikhonov ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computers & Geosciences
سال: 2017
ISSN: 0098-3004
DOI: 10.1016/j.cageo.2017.04.010