Neural networks for non-linear parameter estimation in SDE with memory.
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Updated
Mar 1, 2018 - Python
Neural networks for non-linear parameter estimation in SDE with memory.
A variational method for fast, approximate inference for stochastic differential equations.
Python solver for the Brownian, Stochastic, or Noisy Differential Equations
Schramm-Loewner Evolution Library
Library containing functions for simulating levitated nanoparticles by solving the Stochastic Differential Equation describing their motion.
Effect of hidden nodes on the reconstruction of bidirectional networks
Tensorflow Implementation of Deep Backward Stochastic Differential Equation
Deep multistep methods to solve BSDEs of first and second order for the approximation of PDE solutions
Various Numerical Analysis algorithms for science and engineering.
Predicting stock prices using Geometric Brownian Motion and the Monte Carlo method
Solve an SIS model with SDEs
⚡️ A framework that investigates the scaling limit of ResNets and compares it to Neural ODEs. Tested on synthetic and standardized datasets. 📈
SdePy: Numerical Integration of Ito Stochastic Differential Equations
Time series analysis of COVID-19 data using two unique methods
Codes for numerically solving stochastic inflationary dynamics in slow-roll and beyond.
Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
MC-Simulation of the Ito-SDE (Krülls 1994)
Taylor moment expansion Gaussian filter and smoother in Python (Jax)
Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning (ICML 2022)
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