| Abstract Scope |
In microscopy, the signal recorded for each pixel is composed of the true signal plus noise. No matter how much data is collected, there will always be at least twice as many parameters to estimate as there are pixel measurements, Microscopy, then, constitutes ill-posed problems with many equally valid solutions to the governing equations. Machine learning excels at solving ill-posed problems, but using physics allows us to exceed the performance of off-the-shelf machine learning algorithms. This can be in the form of either forward modeling of the true signal or by “regularizing.” Regularizing allows for a rationale for choosing a particular solution among the many valid ones. This presentation gives examples of phase field regularization for polycrystalline SiC, of fluid dynamics analogues to continuous fibers in SiC/SiC composite materials, and of object symmetries, as predicted from the Curie Principle, used for tuning deep neural networks for Ni dendrite core detection. |