Pages that link to "Item:Q2115712"
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The following pages link to A data-driven approach for discovering stochastic dynamical systems with non-Gaussian Lévy noise (Q2115712):
Displaying 22 items.
- Extracting governing laws from sample path data of non-Gaussian stochastic dynamical systems (Q2076049) (← links)
- ISALT: inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems (Q2129142) (← links)
- Revealing hidden dynamics from time-series data by ODENet (Q2138013) (← links)
- A data-driven method for the steady state of randomly perturbed dynamics (Q2331917) (← links)
- Extracting stochastic dynamical systems with α-stable Lévy noise from data (Q5066035) (← links)
- Discovery of Nonlinear Multiscale Systems: Sampling Strategies and Embeddings (Q5382442) (← links)
- Data-driven approximation for extracting the transition dynamics of a genetic regulatory network with non-Gaussian Lévy noise (Q5880292) (← links)
- A machine learning method for computing quasi-potential of stochastic dynamical systems (Q6048057) (← links)
- Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning (Q6058680) (← links)
- Detecting stochastic governing laws with observation on stationary distributions (Q6102440) (← links)
- Discovering stochastic partial differential equations from limited data using variational Bayes inference (Q6118584) (← links)
- Stochastic dynamics and data science (Q6151506) (← links)
- Data-driven method to extract mean exit time and escape probability for dynamical systems driven by Lévy noises (Q6151512) (← links)
- A deep learning method for computing mean exit time excited by weak Gaussian noise (Q6539426) (← links)
- A data-driven framework for learning hybrid dynamical systems (Q6548679) (← links)
- Data-driven discovery of stochastic dynamical systems with \(\alpha\)-stable Lévy noise based on residual networks (Q6554911) (← links)
- Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems (Q6557792) (← links)
- Learning the temporal evolution of multivariate densities via normalizing flows (Q6560595) (← links)
- Data driven adaptive Gaussian mixture model for solving Fokker-Planck equation (Q6560605) (← links)
- Variational inference of the drift function for stochastic differential equations driven by Lévy processes (Q6565141) (← links)
- An end-to-end deep learning approach for extracting stochastic dynamical systems with \(\alpha\)-stable Lévy noise (Q6565156) (← links)
- Approximation identification for the stochastic time-delayed dynamical system (Q6638497) (← links)
