Ulam projections approximate equivariant families and linear responses for nonautonomous systems with memory loss and regularizing transfer operators.
2018 Markov State Models: From an Art to a Science.Journal of the American Chemical Society140, 2386–2396
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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.
Extends linear response theory to nonautonomous systems and applies it to optimal fingerprinting for attributing changes to multiple forcings in time-dependent backgrounds, with numerical tests on a climate model.
DeltaDiff is a physics-guided inference method that predicts mutant protein structures from a baseline diffusion model without retraining, tested on three systems with nonlocal changes.
A committor-guided Milestoning (CoM) algorithm using neural-network ansatz and short trajectories for efficient prediction of mean first passage times in biomolecular systems.
citing papers explorer
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Ulam Approximation for Nonautonomous Systems: Equivariant Measures and Linear Response
Ulam projections approximate equivariant families and linear responses for nonautonomous systems with memory loss and regularizing transfer operators.
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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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Linear Response and Optimal Fingerprinting for Nonautonomous Systems
Extends linear response theory to nonautonomous systems and applies it to optimal fingerprinting for attributing changes to multiple forcings in time-dependent backgrounds, with numerical tests on a climate model.
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DeltaDiff: Training-Free, Physics-Guided Machine Learning for Predicting Mutant Protein Structures
DeltaDiff is a physics-guided inference method that predicts mutant protein structures from a baseline diffusion model without retraining, tested on three systems with nonlocal changes.
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Fast and accurate committor estimation for kinetics simulations
A committor-guided Milestoning (CoM) algorithm using neural-network ansatz and short trajectories for efficient prediction of mean first passage times in biomolecular systems.