A pipeline combining importance sampling with Markov state models, chain-rule sensitivities, and RiteWeight reweighting enables efficient parameter optimization for rare-event dynamics in nonequilibrium systems.
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Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.
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Sensitivity Analysis in the Face of Rare Events
A pipeline combining importance sampling with Markov state models, chain-rule sensitivities, and RiteWeight reweighting enables efficient parameter optimization for rare-event dynamics in nonequilibrium systems.
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A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
Bayesian optimization with Gaussian processes unifies minimization, single-point saddle searches, and double-ended path searches on potential energy surfaces through a shared six-step surrogate loop using derivative observations and inverse-distance kernels.