A Curated List of Awesome Works in World Modeling, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in World Modeling.
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Updated
Jun 30, 2026
A Curated List of Awesome Works in World Modeling, Aiming to Serve as a One-stop Resource for Researchers, Practitioners, and Enthusiasts Interested in World Modeling.
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
A package for the sparse identification of nonlinear dynamical systems from data
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.
Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Award winning software library for nonlinear dynamics and nonlinear timeseries analysis
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
A Control Systems Toolbox for Julia
A Python Package For System Identification Using NARMAX Models
Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses
Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
Python package for solving partial differential equations using finite differences.
A Python native dynamical system simulation framework in the block diagram paradigm.
Arrays with arbitrarily nested named components.
Code for the paper "Learning Differential Equations that are Easy to Solve"
Simulate dynamic systems expressed in block diagram form using Python
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