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Book Summary
Continuing advances in computer technology have made it possible for engineers
and scientists to construct increasingly realistic models of physical processes.
Practical Inverse Analysis in Engineering addresses an important area of
engineering that will become even more significant to engineers and scientists -
combining measurements with engineering models. This self-contained text
presents applied mathematical tools for bridging the gap between real-world
measurements and mathematical models.
The book demonstrates how to treat "ill-conditioned" inverse analysis problems -
those problems where the solution is extremely sensitive to the data - with the
powerful theory of dynamic programming. A second theory,
generalized-cross-validation, is also discussed as a useful partner in handling
real data.
Continuing advances in computer technology have made it possible for engineers
and scientists to construct increasingly realistic models of physical processes.
Practical Inverse Analysis in Engineering addresses an important area of
engineering that will become even more significant to engineers and scientists -
combining measurements with engineering models. This self-contained text
presents applied mathematical tools for bridging the gap between real-world
measurements and mathematical models.
The book demonstrates how to treat "ill-conditioned" inverse analysis problems -
those problems where the solution is extremely sensitive to the data - with the
powerful theory of dynamic programming. A second theory,
generalized-cross-validation, is also discussed as a useful partner in handling
real data.