GPU-accelerated reservoir simulation, physics-informed neural operators, and Bayesian inverse problems. Building the next generation of scientific machine learning for subsurface flow and carbon capture.
I am a Senior DevTech Engineer in the Energy team at NVIDIA, working at the intersection of GPU computing, scientific machine learning, and subsurface physics. My research focuses on making physics-informed neural operators fast and accurate enough to replace traditional numerical simulators in real-world energy workflows — from reservoir management to carbon capture and storage at scale.
My PhD at the University of Manchester (supervised by Oliver Dorn and Rossmary Villegas, with a postdoc under Kody Law) established a deep foundation in Bayesian inverse problems, ensemble Kalman methods, and surrogate modelling. The central challenge: given noisy observations y, recover the unknown parameter field u by characterising the posterior
π(u | y) ∝ exp(−½‖G(u) − y‖²Γ) · π₀(u)
where G : u ↦ y is the forward operator — classically a full black-oil PDE solve, now replaced by a learned neural surrogate. This work is applied directly to reservoir history matching, CO₂ plume migration, and ensemble-based inversion (ES-MDA, aREKI) with generative priors.
I developed the CCR (Cluster Classify Regress) framework for learning highly nonlinear discontinuous functions across sharp phase boundaries — a persistent failure mode for standard regression. Building on this, I have applied PINO and FNO-based surrogates to the Norne field black-oil benchmark (46×112×22 grid, multi-phase, multi-well), solving the parametric PDE family
∂t(φ Sα) + ∇·Fα(S, p, K) = qα
across thousands of permeability realisations in a single forward pass, achieving up to 6000× speedup over conventional simulators. I contribute to the NVIDIA PhysicsNeMo open-source framework.