Learning Chemical Reaction Networks from Trajectory Data
DOI10.1137/19M1265880zbMath1431.92047arXiv1902.04920OpenAlexW2989086808MaRDI QIDQ5207548
Wei Zhang, Christof Schütte, Stefan Klus, Tim O. F. Conrad
Publication date: 3 January 2020
Published in: SIAM Journal on Applied Dynamical Systems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1902.04920
inverse problemschemical reactionsasymptotic analysisdata-driven methods${{l}_{1}}$ sparse optimization
Epidemiology (92D30) Population dynamics (general) (92D25) Systems biology, networks (92C42) Applications of continuous-time Markov processes on discrete state spaces (60J28) Inference from stochastic processes and fuzziness (62M86)
Uses Software
Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
- A tutorial on chemical reaction network dynamics
- Markov chains and stochastic stability
- Asymptotic analysis of multiscale approximations to reaction networks
- Maximum likelihood estimation in the birth-and-death process
- Practical mathematical optimization. Basic optimization theory and gradient-based algorithms
- Optimal control of Markov jump processes: asymptotic analysis, algorithms and applications to the modeling of chemical reaction systems
- Separation of time-scales and model reduction for stochastic reaction networks
- High-dimensional Bayesian parameter estimation: case study for a model of JAK2/STAT5 signaling
- Introduction to Nonsmooth Optimization
- The Predictive Sample Reuse Method with Applications
- Maximum likelihood estimation for continuous-time stochastic processes
- Asymptotic Statistics
- Regression Shrinkage and Selection via The Lasso: A Retrospective
- Note on the Consistency of the Maximum Likelihood Estimate
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