Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank reduced operator typically obtained by singular value decomposition the For parameter-dependent models, as found in many multi-query applications such uncertainty quantification design optimization, onl...

Surrogate models based on convolutional neural networks (CNNs) for computational fluid dynamics (CFD) simulations are investigated. In particular, the flow field inside two-dimensional channels with a sudden expansion and an obstacle is predicted using image representation of geometry as input. Generative adversarial (GANs) have been shown to excel at such image-to-image translation tasks. This...

A case-based reasoning system was created to support customers who purchased appliances from General Electric. When a customer calls General Electric for help, a call-taker uses the system to diagnose the problem and step the customer through its solution. The system has been in use by 300 call-takers since 1999. It has resulted in a 20 percent increase in the probability the customer’s problem...