Projects
Paper Club
The «Paper Club» is a small circle of interested students meeting once a month to read and discuss papers from diverse scientific fields. The following table serves as a reference and lists the papers we have read in previous meetings.
| Date | Authors | Title |
|---|---|---|
| September 25, 2017 | Guy Katz, Clark Barrett, David Dill, Kyle Julian, Mykel Kochenderfer | Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks |
| October 27, 2017 | Martin Gander, Gerhard Wanner | From Euler, Ritz, and Galerkin to Modern Computing |
| December 4, 2017 | Satoshi Nakamoto | Bitcoin: A Peer-to-Peer Electronic Cash System |
| January 3, 2018 | Tianqi Chen, Carlos Guestrin | XGBoost: A Scalable Tree Boosting System |
| January 29, 2018 | Miguel Alcubierre | The warp drive: hyper-fast travel within general relativity |
| March 7, 2018 | Maurizio Falcone, Roberto Ferretti | Semi-Lagrangian Approximation Schemes for Linear and Hamilton-Jacobi Equations — Chapter 8: Control and games |
| April 11, 2018 | Dave Bayer, Persi Diaconis | Trailing the Dovetail Shuffle to its Lair (I) |
| May 1, 2018 | Dave Bayer, Persi Diaconis | Trailing the Dovetail Shuffle to its Lair (II) |
PAPP
Project AFEM Plus Plus (PAPP) is aimed at implementing a fast, yet practical FEM library written in C++. It allows
- fast adaptive n-dimensional refinements
- an easy way to extend the library (custom refinement schemes/finite elements)
- an easy to use FFI
Xerus
The xerus library is a general purpose library for numerical calculations with higher order tensors, Tensor-Train Decompositions and general Tensor Networks. The focus of development was the simple usability and adaptibility to any setting that requires higher order tensors or decompositions thereof.
- Modern code and concepts incorporating many features of the C++11 standard.
- Full python bindings with very similar syntax for easy transitions from and to C++.
- Calculation with tensors of arbitrary orders using an intuitive Einstein-like notation
A(i,j) = B(i,k,l) * C(k,j,l);. - Full implementation of the Tensor-Train decompositions (MPS) with all neccessary capabilities (including Algorithms like ALS, ADF and CG).
- Lazy evaluation of (multiple) tensor contractions featuring heuristics to automatically find efficient contraction orders.
- Direct integration of the blas and lapack, as high performance linear algebra backends.
- Fast sparse tensor calculation by usage of the suiteSparse sparse matrix capabilities.
- Capabilites to handle arbitrary Tensor Networks.