Data-Driven Model Order Reduction, Parameter Estimation, and Prediction
- Discovered reduced sets of efective parameters with which to describe dynamic models.
- Confirmed equivalence of data-driven parameters to analytical expressions proposed by experts.
- Used reduced representations to predict unseen system behaviors and parameter values.
- Implemented conformal autoencoders to disentangle effective from redundant parameters.
Machine Learning for Materials Science
Parameterizing Structure-Process-Property Relations in Data
- Constructed machine learning models of zeolite catalyst properties from small data set.
- Used models to propose new synthesis conditions that improved catalytic performance.
- Co-developed an active learning scheme for exploration of high-entropy alloy lattice space.
- Including dimensionality reduction and comparing performance to composition-based approach.
Manifold Learning for Accelerated Solution of High-Dimensional Problems
- Analyzed trajectories of simulated annealing as time series from Langevin dynamics.
- Combined diffusion maps with SDE parameter inference to inform larger, coarse-grained steps.
- Achieved convergence with fewer objective evaluations than standard simulated annealing.
- Extending approach to Bayesian optimization on reduced space of diffusion coordinates.