Automated model building and representation learning for multiscale simulations
(B07)
Subject Area
Statistical Physics, Nonlinear Dynamics, Complex Systems, Soft and Fluid Matter, Biological Physics
Term
since 2018
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 233630050
The project addresses applications of machine learning techniques to multi-scale simulation of soft-matter systems. We focus on two aspects: The first is to build a coarser-grained model, e.g., using generic function approximators such as kernels or deep neural networks to learn latent representations of the coarser level dynamics from data. The second aspect is back-mapping, i.e., the reconstruction of a high-resolution simulation state corresponding to the coarse-grained state. In the next funding period, one main goal for both aspects will be efficiency. In addition, we will explore new applications for backmapping, where the coarse-scale information might come from different data sources.
DFG Programme
CRC/Transregios
International Connection
Netherlands
