Abstract
Editorial: Data Science Applications to Inverse and Optimization Problems in Earth Science
Highlights
Specialty section: This article was submitted to Optimization, a section of the journal Frontiers in Applied Mathematics and Statistics
Solving inverse and optimization problems that are encountered in the earth sciences is often challenging because of the computational cost of simulating models, the nonlinearity of forward models, the frequently large number of uncertain parameters or decision options and the limited information provided by data
These challenges have motivated a significant investment of effort into the development of efficient methods to improve the efficacy and reduce the overall computational costs of inversion and optimization workflows
Summary
Specialty section: This article was submitted to Optimization, a section of the journal Frontiers in Applied Mathematics and Statistics. Data Science Applications to Inverse and Optimization Problems in Earth Science Solving inverse and optimization problems that are encountered in the earth sciences is often challenging because of the computational cost of simulating models, the nonlinearity of forward models, the frequently large number of uncertain parameters or decision options and the limited information provided by data.
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