Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
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Updated
Jun 29, 2024 - Julia
Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem
Build and simulate jump equations like Gillespie simulations and jump diffusions with constant and state-dependent rates and mix with differential equations and scientific machine learning (SciML)
Simulate stochastic timeseries that follow ARFIMA, ARMA, ARIMA, AR, etc. processes
Differential equation problem specifications and scientific machine learning for common financial models
🕷️ Non-Markovian stochastic SPIn (and harmonic oscillator) DYnamics.
Algorithmic music composition in Julia
A Julia package to generate multisite weather sequences
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