11 citations · 12 across the 5 of their papers we have counts for
3 papers · 1 filter
Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Katharine Fisher, Michael Herbst, James Kermode +1
Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem…
Stein Kernelized Molecular Dynamics for Active Learning of Interatomic Potentials
Joanna Zou, Fraser Birks, Dallas Foster +1
Machine learning interatomic potentials (MLIPs) enable efficient and accurate atomistic simulations but depend critically on the quality and diversity of the training data. We intr…
Goal-oriented learning of stochastic differential equations using error bounds on path-space observables
Joanna Zou, Han Cheng Lie, Youssef Marzouk
Stochastic differential equations (SDEs), which serve as the governing equations for dynamical systems in a broad range of applications, can become cost-prohibitive for numerical s…