Research
My research focuses on the magnetic behaviour of complex materials, using advanced numerical modelling, micromagnetic simulation, and artificial intelligence. I develop and apply high performance finite element and machine learning methods to study how non uniform magnetic domain structures in minerals, such as vortex and multi domain states, arise and how they influence the stability of magnetic remanence. A recent example is FORCINN, a neural network based approach for interpreting first order reversal curve (FORC) data, which links measured magnetic responses to underlying grain and domain state distributions in geologically significant minerals. Together, these methods are used to understand how micro and nanoscale magnetic configurations in rocks and other functional magnetic materials control their ability to record and retain magnetic signals.
Selected highlights of my research include demonstrating that non uniform single vortex domain structures in geologically significant minerals can provide stable magnetic remanence over very long timescales, developing FORCINN, a neural network based approach for interpreting first order reversal curve (FORC) data that links measured magnetic responses to underlying grain and domain state distributions, and using large scale micromagnetic and finite element simulations to connect nanoscale domain configurations with bulk magnetic measurements and material microstructure.
Research grants
NSFGEO-NERC: The history of the Earth's magnetic field strength over the last five million years: Filling in the southern hemisphere gap
NATURAL ENVIRONMENT RESEARCH COUNCIL
January 2024 - June 2026
MicroPI: A micromagnetic approach to absolute palaeointensity determinations
NATURAL ENVIRONMENT RESEARCH COUNCIL
June 2024 - May 2027
VIRGIL: The VIRtual paleomaGnetIc Laboratory
NATURAL ENVIRONMENT RESEARCH COUNCIL
March 2022 - February 2027