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Found 4 papers, 3 papers with code
Deeptime: a Python library for machine learning dynamical models from time series data
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics.
Generating valid Euclidean distance matrices
Generating point clouds, e. g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task.
Megaphone: Live state migration for distributed streaming dataflows
We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives.
Distributed, Parallel, and Cluster Computing
Variational Selection of Features for Molecular Kinetics
The modeling of atomistic biomolecular simulations using kinetic models such as Markov state models (MSMs) has had many notable algorithmic advances in recent years.
