Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods utilize mathematical …

Signal processing, communications, and control have traditionally relied on classical
statistical modeling techniques. Such model-based methods tend to be sensitive to …

In recent years, deep learning has proven to be a viable methodology for surrogate
modeling and uncertainty quantification for a vast number of physical systems. However, in …

Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …

Three important properties of a classification machinery are i) the system preserves the core
information of the input data; ii) the training examples convey information about unseen …

Control of complex systems involves both system identification and controller design. Deep
neural networks have proven to be successful in many identification tasks, however, from …

Deep learning has revolutionized pattern recognition, introducing tools that power a wide
range of technologies in such diverse fields as computer vision, natural language …

Neural networks are known to be effective function approximators. Recently, deep neural
networks have proven to be very effective in pattern recognition, classification tasks and …

Deep learning has proven itself to be a powerful tool to develop datadriven signal
processing algorithms for challenging engineering problems. By learning the key features …

Standard neural networks can approximate general nonlinear operators, represented either
explicitly by a combination of mathematical operators, eg in an advection–diffusion reaction …