Predicting rare events using neural networks and short-trajectory data
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Publication:6104999
DOI10.1016/j.jcp.2023.112152arXiv2208.01717OpenAlexW4375948460MaRDI QIDQ6104999
John Strahan, Justin Finkel, Aaron R. Dinner, Jonathan Weare
Publication date: 16 June 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2208.01717
Stochastic analysis (60Hxx) Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx)
Cites Work
- Unnamed Item
- A data-driven approximation of the koopman operator: extending dynamic mode decomposition
- From classical dynamics to continuous time random walks
- Dimensionality reduction of complex metastable systems via kernel embeddings of transition manifolds
- Committor functions via tensor networks
- Solving high-dimensional eigenvalue problems using deep neural networks: a diffusion Monte Carlo like approach
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Solving for high-dimensional committor functions using artificial neural networks
- Diffusion maps
- Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations
- Rational Construction of Stochastic Numerical Methods for Molecular Sampling
- Solving high-dimensional partial differential equations using deep learning
- Coupling rare event algorithms with data-based learned committor functions using the analogue Markov chain
- Stratification as a General Variance Reduction Method for Markov Chain Monte Carlo
- Stochastic Processes and Applications
- Deep learning in fluid dynamics
- Statistics of Extreme Events in Fluid Flows and Waves
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