I am broadly interested in the numerical details of smoothed particle hydrodynamics (SPH) simulations. Astrophysics is a soup combining a multiple types of physics. My research involves creating and testing multi-physics SPH algorithms, that is, SPH methods that simultaneously solve hydrodynamics which is coupled to other physical processes. A key line of research in this area has been the development of magnetohydrodynamics methods for SPH.

Check out my review article on SPMHD.

Simulations are computationally expensive. Architectures and super-computing clusters continue to increase in scale. Making efficient use of these resources requires highly-effective parallel schemes, so that memory and communication overhead do not bottleneck a computation. I am interested in developing shared-memory threaded schemes that can scale to large core numbers, and in developing distributed computing algorithms for SPH codes that can scale to hundreds or thousands of nodes of a cluster.

All simulations are underpinned by the quality of their methods. I conduct experimental studies to understand and proof the numerical properties of SPH, to identify items such as the root cause of numerical errors and in what physical regimes a numerical method may or may not be suitable. For example, I have studied the convergence properties of SPH on the linear and non-linear evolution of the Kelvin-Helmholtz instability, demonstrating the numerical conditions under which convergent solutions may be obtained.

Synthetic data is artifically created data that looks like real data, both on an individual sample level, its statistical properties, and its complex non-linear relationships. Synthetic data presents an option to work with highly-sensitive forms of data where there exists strong privacy or security concerns. I am interested in the use of generative deep learning to create synthetic data of financial and healthcare data.

Phantom is our a high-performance astrophysical simulation code using smoothed particle hydrodynamics. It includes a range of different physics relevant for the general study of astrophysics in the galaxy. I am a development lead for Phantom. Join us on slack or at one of our user's group meetings.

Sarracen is a Python library for SPH visualization and analysis. Sarracen can visualize SPH data and be used interactively in a Jupyter notebook environment. It read the file format of several different astrophysical SPH codes, and comes bundled with routines for common astrophysical data analyses. Sarracen supports multi-threaded and GPU (through CUDA) visualization. Our goal is to provide support for the Python visualization and analysis of SPH data in a natively SPH way using the smoothing kernel.

Framalytics is a Python package to work with the Functional Resonance Analysis Method (FRAM). Framalytics can read FRAM models specified by the .xfmv file format created by the FRAM Model Visualizer (FMV), and provides a general interface to interact with FRAM models in Python. Our goal is to enable the interaction of FRAM models with data science and machine learning tools.