Showing 4 open source projects for "differential equation solver"

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    Diffrax

    Diffrax

    Numerical differential equation solvers in JAX

    Diffrax is a numerical differential equation solving library built for the JAX ecosystem, with a strong focus on composability, differentiability, and high-performance scientific computing. The project provides tools for solving ordinary differential equations, stochastic differential equations, controlled differential equations, and related systems in a way that fits naturally into modern machine learning and differentiable programming workflows. ...
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  • 2
    DataDrivenDiffEq.jl

    DataDrivenDiffEq.jl

    Data driven modeling and automated discovery of dynamical systems

    DataDrivenDiffEq.jl is a package for finding systems of equations automatically from a dataset. The methods in this package take in data and return the model which generated the data. A known model is not required as input. These methods can estimate equation-free and equation-based models for discrete, continuous differential equations or direct mappings.
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  • 3
    DPM-Solver

    DPM-Solver

    Fast ODE Solver for Diffusion Probabilistic Model Sampling

    ...The project introduces a specialized numerical solver designed to approximate the diffusion process using a small number of high-order integration steps. By reformulating the sampling problem as the solution of a diffusion-related ordinary differential equation, the solver can produce high-quality samples much more efficiently. This approach significantly reduces the computational cost required to generate images while maintaining strong generation quality.
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  • 4
    PyTorch Implementation of SDE Solvers

    PyTorch Implementation of SDE Solvers

    Differentiable SDE solvers with GPU support and efficient sensitivity

    This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1].
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