Description
Chairperson: Edward Hutter, UIUC/NCSA
Proteins and other biological molecules are responsible for many vital cellular functions, such as transport, signaling, or catalysis, and dysfunction can result in diseases. Information on the 3-dimensional (3D) structures of biological molecules and their dynamics is essential to understand mechanisms of their functions, leading to medicinal applications such as drug design. Different...
This JLESC collaboration focuses on the prediction of flow fields using machine learning (ML) techniques. The basis for the project are jointly developed convolutional neural networks (CNNs) with an autoencoder-decoder type architecture, inspired by the work in [1]. These CNNs are used to investigate dimension-reduction techniques for a three-dimensional flow field [2]. That is, the CNNs are...
Super-resolution networks (SRNs) are employed for enhancing the resolution of Computer Tomography (CT) images. In previous works of the JSC group, respiratory flow simulations were integrated into a data processing pipeline to facilitate diagnosis and treatment planning in rhinology [1]. However, obtaining accurate simulation results is often hindered by low CT image resolutions in clinical...
Deep Learning (DL) emerged as a way to extract valuable information from ever-growing volumes of data. However, when trained on sequential tasks ie. without full access to the dataset at the beginning of the training, typical Deep Neural Networks (DNNs) suffer from catastrophic forgetting, a phenomenon causing them to reinforce new patterns at the expense of previously acquired knowledge....
