Neural network-based parameter estimation of stochastic differential equations driven by Lévy noise
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Publication:2088244
DOI10.1016/j.physa.2022.128146OpenAlexW4294579762WikidataQ115341715 ScholiaQ115341715MaRDI QIDQ2088244
Publication date: 21 October 2022
Published in: Physica A (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physa.2022.128146
stochastic differential equationparameter estimationneural networkOrnstein-Uhlenbeck processLévy noiseDuffing system
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- Maximum likelihood estimation in processes of Ornstein-Uhlenbeck type
- Least squares estimator for discretely observed Ornstein-Uhlenbeck processes with small Lévy noises
- Quantifying the parameter dependent basin of the unsafe regime of asymmetric Lévy-noise-induced critical transitions
- Mutual relevance of investor sentiment and finance by modeling coupled stochastic systems with MARS
- Parameter identification for van der Pol-Duffing oscillator by a novel artificial bee colony algorithm with differential evolution operators
- On a continuous analogue of the stochastic difference equation \(X_ n\) = rho X//(n-1) + \(B_ n\).
- Statistical inference for ergodic diffusion processes.
- On the Chambers-Mallows-Stuck method for simulating skewed stable random variables
- Generalized moment estimators for \(\alpha\)-stable Ornstein-Uhlenbeck motions from discrete observations
- First-passage problem for stochastic differential equations with combined parametric Gaussian and Lévy white noises via path integral method
- Phase transition and alternation in a model of perceptual bistability in the presence of Lévy noise
- Least squares estimator for Ornstein-Uhlenbeck processes driven by \(\alpha \)-stable motions
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- The phase transition in a bistable Duffing system driven by Lévy noise
- Approximation of stochastic differential equations driven by α-stable Lévy motion
- A Method for Simulating Stable Random Variables
- Lévy Processes and Stochastic Calculus
- Extracting stochastic dynamical systems with α-stable Lévy noise from data
- A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
- Stochastic Differential Equations
- Classification, inference and segmentation of anomalous diffusion with recurrent neural networks
- Characterization of anomalous diffusion classical statistics powered by deep learning (CONDOR)