Reference Books

      Deep Learning (Adaptive Computation and Machine Learning series)

      Elementary Numerical Analysis: An Algorithmic Approach (Updated with MATLAB), by S. D. Conte and Carl de Boor.
      Fundamentals of Numerical Computation, by Tobin A. Driscoll and Richard J. Braun.
      The Finite Element Method for Elliptic Problems, by Philippe G. Ciarlet.
      Finite Element Solution of Boundary Value Problems, by O. Axelsson and V. A. Barker.
      Iterative Solution of Nonlinear Equations in Several Variables, by J. M. Ortega and W. C. Rheinboldt.
      Numerical Methods for Evolutionary Differential Equations, by Uri M. Ascher.
      Computational Methods for Multiphase Flows in Porous Media, by Zhangxin Chen, Guanren Huan, and Yuanle Ma.
      Discontinuous Galerkin Methods for Solving Elliptic and Parabolic Equations: Theory and Implementation, by Betrice Riviere.
      Iterative Methods for Linear and Nonlinear Equations, by C. T. Kelley.

      Mathematical and Computational Techniques for Multilevel Adaptive Methods, by Ulrich Rude.
      Multilevel Adaptive Methods for Partial Differential Equations, by Stephen F. McCormick.
      The Mathematics of Reservoir Simulation, by Richard E. Ewing.

      Solving Nonlinear Equations with Newton's Method, by C. T. Kelley.
      Accuracy and Stability of Numerical Algorithms, Second Edition, by Nicholas J. Higham.
      A Multigrid Tutorial, Second Edition, by William L. Briggs, Van Emden Henson, Steve F. McCormick.

      Multigrid Methods, by Stephen F. McCormick.

      Linear Algebra with Applications, by Steven J. Leon.

      Matrix computations (3rd. ed), by Gene Howard Golub, Charles F. Van Loan.

      Numerical Linear Algebra and Applications, by Biswa Nath Datta.

      Fluid-structure Interactions. Models, Analysis and Finite Elements, by Thomas Richter, Springer 2017, 1st Edition.