In response to an increasing demand for novel computing methods, Neural Networks for Applied Sciences and Engineering provides a simple but systematic introduction to neural networks applications. This book features case studies that use real data to demonstrate practical applications. It contains in-depth discussions of data and model validation issues along with uncertainty and sensitivity assessment of models as well as data dimensionality and methods to reduce dimensionality. It provides detailed coverage of neural network types for extracting nonlinear patterns in multi-dimensional scientific data in prediction, classification, clustering and forecasting with an extensive coverage on linear networks, multi-layer perceptron, self organization maps, and recurrent networks.