Pre-Print
Accelerated Amine Based Carbon Capture Performance Optimization Leveraging A Fully Differentiable Neural Surrogate
// Research
Research directions & publications
My work spans scientific computing, dynamical systems and deep learning, with an emphasis on developing models that are provably reliable and practically deployable for performance optimization and control, specifically on resource constrained hardware. Learn more about my current interests below!
neural-operators
Neural Operators for multiphysics problems
Learning infinite-dimensional operators that map initial conditions to time dependent trajectories for multiphysics systems, enabling for cheap-to-inference predictions of future dynamics.
FNODeepONetOperator Learning
UQ
Uncertainty Quantification for Learned Operators
Im actively exploring how we can guarantee the quality of predictions from learned operator representations, something which is necessary for use in safety critical applications.
UQReliability
Neural-MPC
Neural Surrogate Model Predictive Control
Replacing expensive first-principles solvers inside MPC loops with structure-preserving neural surrogates, targeting real-time feasibility for systems governed by stiff or high-dimensional dynamics.
MPCSurrogate ModellingReal-Time Control
All Publications