Summary
I am a Schmidt Futures AI in Science postdoctoral fellow at I-X. My research is focused on the theory and numerical modelling of the observable signals in high energy density plasma and inertial confinement fusion experiments. The physics areas of particular interest to me are: neutron and radiation transport, diagnostic techniques, hydrodynamics and the properties of non-ideal plasmas. I am also interested in the application of AI/ML techniques in plasma physics, particularly in the inference process. These include Differentiable Programming, Markov Chain Monte Carlo, Gaussian Processes and Neural Networks.
I am also the deputy head of the 1st year undergraduate computing course which teaches students the basics of Python and its use in scientific computing. I also lecture a short post-graduate Machine Learning course in the Plasma Group (Slides & Code here).
My open source projects:
Neutron spectroscopy Python code - NeSST
- Calculates primary and scattered neutron spectra for analyzing ICF experimental data.
Optical Thomson scattering spectra PythonJAX code - OTSax
- Utilising differentiable programming to allow gradient descent methods for efficient fitting of experimental OTS data for non-Maxwellian ion distributions.
UROP open source projects:
1D electromagnetic particle-in-cell code in JAX by Sean Lim - PiC-Code-Jax
Selected Publications
Journal Articles
Crilly AJ, Appelbe BD, Mannion OM, et al. , 2020, Neutron backscatter edge: A measure of the hydrodynamic properties of the dense DT fuel at stagnation in ICF experiments, Physics of Plasmas, Vol:27, ISSN:1070-664X, Pages:012701-1-012701-11
Crilly AJ, Appelbe BD, McGlinchey K, et al. , 2018, Synthetic nuclear diagnostics for inferring plasma properties of inertial confinement fusion implosions, Physics of Plasmas, Vol:25, ISSN:1070-664X