ThGaskin / NeuralABMLinks
Neural parameter calibration for multi-agent models. Uses neural networks to estimate marginal densities on parameters and networks
☆31Updated last week
Alternatives and similar repositories for NeuralABM
Users that are interested in NeuralABM are comparing it to the libraries listed below
Sorting:
- Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high perf…☆233Updated 2 years ago
- Package for fitting Gaussian Process Emulators to multiple output computer simulation results.☆51Updated last year
- Stochastic Optimization under Uncertainty in Python.☆36Updated 3 months ago
- Implementation of a reservoir computer with functions to optimize the parameters and calculate the Lyapunov exponents☆30Updated 3 years ago
- Symbolic Identification of Non-linear Dynamics. The method generalizes the SINDy algorithm by combining sparse and genetic-programming-ba…☆76Updated 3 years ago
- A framework for composing Neural Processes in Python☆85Updated 8 months ago
- Monotone Parameterization Toolkit (MParT): A core library for constructing and using transport maps.☆18Updated last year
- Kernel methods for statistical modeling of dynamical systems☆25Updated last week
- Example code for paper: Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributio…☆56Updated 3 years ago
- Implements Optimization and approximate uncertainty quantification algorithms, Ensemble Kalman Inversion, and Ensemble Kalman Processes.☆106Updated 3 weeks ago
- How to train a neural ODE for time series/weather forecasting☆39Updated 2 years ago
- Python package to compute Lyapunov exponents, covariant Lyapunov vectors (CLV) and adjoints of a dynamical systems.☆24Updated last year
- Probabilistic numerical finite differences. Compute finite difference weights and differentiation matrices on scattered data sites and wi…☆11Updated 2 years ago
- Code for the Paper "Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers"☆30Updated last year
- Data Science for Dynamical System Course☆124Updated 11 months ago
- Code accompanying the NeurIPS 2021 Paper: A Probabilistic State Space Model for Joint Inference from Differential Equations and Data (Sch…☆13Updated 2 years ago
- Multi-Output Gaussian Process Toolkit☆176Updated 3 months ago
- ☆39Updated 3 years ago
- Literature and light wrappers for gaussian process models.☆48Updated 4 years ago
- Reservoir computing utilities for scientific machine learning (SciML)☆215Updated this week
- Probabilistic Numerical Differential Equation solvers via Bayesian filtering and smoothing☆129Updated 3 weeks ago
- IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)☆42Updated 2 years ago
- kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order☆74Updated 8 months ago
- Probabilistic ODE solvers are fun, but are they fast? See also: https://github.com/pnkraemer/probdiffeq for JAX code or https://github.c…☆21Updated last year
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆26Updated 3 years ago
- epydemix, the ABC of epidemics☆42Updated 2 weeks ago
- Nonlinear Dynamics: A concise introduction interlaced with code☆245Updated 2 months ago
- ☆110Updated last week
- Probabilistic solvers for differential equations in JAX. Adaptive ODE solvers with calibration, state-space model factorisations, and cus…☆52Updated this week
- 18.S096 - Applications of Scientific Machine Learning☆311Updated 3 years ago