Paul J. Atzberger | Research Group
Home People Publications Talks Software Gallery Teaching News Positions Intranet


Scientific Computation

Mango-Selm : Fluctuating hydrodynamics package for LAMMPS (USER-SELM)

Stochastic Immersed Boundary Methods
Stochastic Eulerian Lagrangian Methods
Implicit-Solvent Coarse-Grained Models

Fluctuating hydrodynamics methods implemented for performing simulations in LAMMPS. The package can be used for performing implicit-solvent coarse-grained simulations in molecular dynamics and for more general problems involving fluid-structure interactions subject to thermal fluctuations. LAMMPS is an easy to use dynamics package for modeling mechanical systems and provides many types of degrees of freedom, common force laws, and statistical analysis methods. Mango-Selm (USER-SELM) allows for using these capabilities when performing fluctuating hydrodynamics simulations. For more details, see

[downloads and additional information].

Stochastic Immersed Boundary Method

Stochastic Immersed Boundary Methods provide approaches for fluid-structure interactions subject to thermal fluctuations. An implementation and tutorial for how to use these methods is provided here:

[downloads and additional information].

Machine Learning


[software] [documentation] [examples]

Download Latest Release

GMLS-Nets: A Framework for Learning from Unstructured Data, N. Trask, R. G. Patel, B. J. Gross, and P. J. Atzberger, (submitted), (2019), [preprint] [arXiv].

Course Supplemental Materials

Data Science and Machine Learning

Image Classification using Convolutional Neural Networks (course exercise)

Jupyter Notebook Codes | CIFAR10 PDF | MNIST PDF | Data Folder

Facial Recognition and Feature Extraction (course exercise)

Jupyter Notebook Codes | Jupyter PDF | Data Folder | Kaggle: Facial Recognition (SVM) | Kaggle PDF

Machine Learning Course Link

Machine Learning: Foundations and Applications Course (MATH CS 120) [course-link]