Literatur für neue Studenten und Doktoranden

Our research activities combine elements of physical modeling, mechanics,  materials, applied mathematics, probability and computation. In order to contribute in specific projects, all graduate students must acquire a strong foundation in the areas above, with different relative emphasis depending on the interests and goals.

The following includes a list of suggested readings that would be helpful in achieving this goal. Some of the books have a significant overlap but naturally there are several other sources which are not mentioned.


Suggested readings in Physics and Mechanics of Materials:

- Introduction to Continuum Mechanics, W Michael Lai, David Rubin, Erhard Krempl
- Continuum Mechanics for Engineers, G. Thomas Mase, Ronald E. Smelser, George E. Mase
- Computational inelasticity, by J.C. Simo & T.J.R. Hughes

- Statistical Mechanics of Elasticity, J.H. Weiner
- Statistical Mechanics: Algorithms and Computations, Werner Krauth
- Statistical Mechanics, Donald Allan McQuarrie
- Nonequilibrium Statistical Mechanics, Robert Zwanzig    

- The Art of Molecular Dynamics Simulation, D. C. Rapaport
- A Guide to Monte Carlo Simulations in Statistical Physics, David P. Landau, Kurt Binder
- Atomic and Electronic Structure of Solids, Efthimios Kaxiras
- Integrated Computational Materials Engineering (ICME) for Metals: Using Multiscale Modeling to Invigorate Engineering Design    with Science,  Mark F. Horstemeyer
 

Suggested readings in Deterministic Applied and Computational Mathematics:

- Introduction to Applied Mathematics, by G. Strang
- Matrix computations, by G.H. Golub & C.van Loan
- Partial Differential Equations, by L. C. Evans

- Stochastic Tools in Mathematics and Science, Chorin, Alexandre J, Hald, Ole H

- Elements of the Theory of Functions and Functional Analysis, by A. N. Kolmogorov, S. V. Fomin  
- Elements of Information Theory, Thomas M. Cover, Joy A. Thomas
    
- The Finite element method: Linear static and dynamic finite element analysis , by T.J.R. Hughes
- An introduction to the finite element method, J.N. Reddy
- A First Course in Finite Elements, Jacob Fish, Ted Belytschko
- Nonlinear Finite Elements for Continua and Structures, Ted Belytschko, Wing Kam Liu, Brian Moran
    
- Numerical optimization, by J. Nocedal,  S. Wright
- Introduction to Optimum Design, Jasbir S. Arora
- Topology Optimization: Theory, Methods and Applications, M. P. Bendsoe, O. Sigmund


Suggested readings in Stochastic Modeling and Machine Learning:

- Introduction to probability models, by S.M. Ross
- Stochastic Tools in Mathematics and Science, Alexandre J. Chorin and Ole H. Hald
- Probability and Measure, P. Billingsley

- Monte Carlo Statistical Methods, by CP. Robert.
- Monte Carlo strategies in scientific computing, by J. S. Liu
- Monte Carlo Methods, Malvin H. Kalos, Paula A. Whitlock
    
- The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, C.P. Robert
- Inference in Hidden Markov Models, Olivier Cappe, Eric Moulines, Tobias Ryden
- Computational and Statistical Methods for Inverse Problems, Jari P. Kaipio and Erkki Somersalo
    
- Pattern Recognition and Machine Learning, by CM Bishop
    
- Stochastic Finite Elements: A Spectral Approach, by R.G. Ghanem & P.D. Spanos


General suggestions

- Learn and use Latex. This will be make your life much easier
- Learn and use BibTex, and start building a .bib file for all your references.
- Learn and use beamer for presentations
- Learn how to program (any language)