Deterministic, Stochastic, and Deep Learning Methods for Computational Electromagnetics

This book provides a well-balanced and comprehensive picture based on clear physics, solid mathematical formulation, and state-of-the-art useful numerical methods (deterministic, stochastic, deep neural network machine learning) for computer simulations of electromagnetic and transport processes in biology, microwave and optical wave devices, and nano-electronics.  Computational research has become strongly influenced by interaction from many different areas including biology, physics, chemistry, engineering, etc.  A well-balanced approach addressing the interconnection among mathematical algorithms (in deterministic, stochastic and machine learning approaches) and physical foundation and application is much needed to prepare graduate students and researchers in applied mathematics and sciences and engineering for innovative advanced computational research in many applications areas, such as biomolecular solvation in solvents, radar wave scattering, the interaction of lights with electrons in metallic materials, quantum dots, electronic structure,  current flows in nano-electronics, and microchip designs, etc.