{"total":23,"rows":[{"id":"2110.15013","title":"Deeptime: a Python library for machine learning dynamical models from time series data","created":"2021-10-28","cited_by_count":38,"area":null},{"id":"2207.05779","title":"Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach","created":"2022-07-12","cited_by_count":15,"area":null},{"id":"2104.11061","title":"Chasing Collective Variables using Autoencoders and biased trajectories","created":"2021-04-22","cited_by_count":13,"area":null},{"id":"2510.07589","title":"Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics","created":"2025-10-08","cited_by_count":11,"area":null},{"id":"2309.07946","title":"Slow Invariant Manifolds of Singularly Perturbed Systems via Physics-Informed Machine Learning","created":"2023-09-14","cited_by_count":10,"area":null},{"id":"2110.03625","title":"Time Series Forecasting Using Manifold Learning","created":"2021-10-07","cited_by_count":7,"area":null},{"id":"2102.05527","title":"Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations","created":"2021-02-10","cited_by_count":5,"area":null},{"id":"2203.09992","title":"Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images","created":"2022-03-18","cited_by_count":4,"area":null},{"id":"2108.12493","title":"Variational embedding of protein folding simulations using gaussian mixture variational autoencoders","created":"2021-08-27","cited_by_count":3,"area":null},{"id":"2110.02296","title":"On the Correspondence between Gaussian Processes and Geometric Harmonics","created":"2021-10-05","cited_by_count":3,"area":null},{"id":"2309.14334","title":"Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points","created":"2023-09-25","cited_by_count":3,"area":null},{"id":"2502.11583","title":"Distributional Autoencoders Know the Score","created":"2025-02-17","cited_by_count":3,"area":null},{"id":"2510.11148","title":"Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials","created":"2025-10-13","cited_by_count":3,"area":null},{"id":"2307.00365","title":"Understanding recent deep-learning techniques for identifying collective variables of molecular dynamics","created":"2023-07-01","cited_by_count":2,"area":null},{"id":"2403.11591","title":"A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs","created":"2024-03-18","cited_by_count":2,"area":null},{"id":"2412.20868","title":"Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniques","created":"2024-12-30","cited_by_count":2,"area":null},{"id":"2306.09111","title":"Enhanced Sampling with Machine Learning: A Review","created":"2023-06-15","cited_by_count":1,"area":null},{"id":"2506.01222","title":"Learning collective variables that preserve transition rates","created":"2025-06-02","cited_by_count":1,"area":null},{"id":"2511.07406","title":"Entangled Schr\\\"odinger Bridge Matching","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2604.25244","title":"Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics","created":"2026-04-28","cited_by_count":1,"area":null},{"id":"2205.10423","title":"Learning Geometrically Disentangled Representations of Protein Folding Simulations","created":"2022-05-20","cited_by_count":0,"area":null},{"id":"2509.17937","title":"Random functions as data compressors for machine learning of molecular processes","created":"2025-09-07","cited_by_count":0,"area":null},{"id":"2602.10637","title":"Coarse-Grained Boltzmann Generators","created":"2026-02-11","cited_by_count":0,"area":null}],"next_cursor":null}