NM
Scientific ML

Neural ODEs to Learn Bloch Dynamics and Replace Differentiable Simulators

A scientific machine learning direction that uses Neural ODEs to approximate Bloch dynamics and reduce reliance on expensive differentiable simulation inside inverse imaging loops.

ResearchResearch blurb

The research direction centers on derivations, simulator comparisons, and ablations on stability, identifiability, and reconstruction quality.

The central question is whether a learned dynamics surrogate can retain the structure needed for inverse problems while becoming faster and easier to optimize.

The visual language on the site treats this project as a field-line system: continuous dynamics, measured signals, and inferred latent states.