In the Visual Computing and Artificial Intelligence Department at MPI for Informatics, we investigate research questions at the intersection of computer graphics, computer vision and artificial intelligence. In this presentation, I will talk about some of the recent work we did on new methods for reconstructing high quality computer graphics models (shape, motion, appearance, material,...
In this work the concepts from scientific machine learning are employed to learn continuum phase field models directly from the experimental data of Scanning Transmission Electron Microscopy (STEM). Currently, we assume the form of the continuum model is known to be as Cahn-Hilliard/Allen-Cahn equations with a prior expression for free energy function. The unknown parameters of the continuum...
Atom probe tomography is now an established near atomic-scale characterization technique. However, the traditional analysis often limits the subtle inherent details of field evaporation processes occurring near defects or multiple phases. We present two cases employing unconventional data mining routines on experimental data to extract valuable physical insights, supported by simulations....
Atom probe tomography (APT) is a unique technique that provides 3D elemental distribution with near atomic resolution for a given material. However, the large amount of data acquired during the experiment and the complexity of the 3D microstructures poses a challenge to fully quantify APT data. Here, taking APT measurements corresponding to a Fe-doped Sm-Co alloy as an example, we present an...
Chemical short-range order (CSRO), referring to specific elements self-organising within a disordered matrix, can modify the properties of materials. CSRO is typically characterized via two-dimensional microscopy techniques that fail to capture three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to reveal three-dimensional imaging of CSRO in...
