Abstract: Machine Learning Force Fields (MLFF) should be accurate, efficient, and applicable to molecules, materials, and interfaces thereof. The first step toward ensuring broad applicability and reliability of MLFFs requires a robust conceptual understanding of how to map interacting electrons to interacting "atoms". Here I discuss two aspects: (1) how electronic interactions are mapped to atoms with a […]
Abstract: We will review physics-informed neural network and summarize available theoretical results.. We will also introduce new NNs that learn functionals and nonlinear operators from functions and corresponding responses for system identification. The universal approximation theorem of operators is suggestive of the potential of NNs in learning from scattered data any continuous operator or complex system. We first […]
Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions from a finite number of samples. When trained successfully, we can use the DGMs to estimate the likelihood of each observation and to create new samples from the underlying distribution. Developing DGMs has become one of the most hotly researched […]
Simulation supports various fields of design and engineering in creating better solutions. Especially, to create a sustainable built environment, complex interdependencies of engineering need to be considered during early design phases. This engineering integration requires the analysis of a high number of variants in early design phases. Lack of information, modelling effort and computation times prohibit such extensive […]