Showing 9 open source projects for "u-root"

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    UnROOT.jl

    UnROOT.jl

    Native Julia I/O package to work with CERN ROOT files objects

    UnROOT.jl is a reader for the CERN ROOT file format written entirely in Julia, without any dependence on ROOT or Python.
    Downloads: 0 This Week
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  • 2
    Polynomials.jl

    Polynomials.jl

    Polynomial manipulations in Julia

    Basic arithmetic, integration, differentiation, evaluation, root finding, and fitting for univariate polynomials in Julia.
    Downloads: 0 This Week
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  • 3
    LineSearches.jl

    LineSearches.jl

    Line search methods for optimization and root-finding

    Line search methods for optimization and root-finding. This package provides an interface to line search algorithms implemented in Julia. The code was originally written as part of Optim, but has now been separated out to its own package.
    Downloads: 0 This Week
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  • 4
    NeuralOperators.jl

    NeuralOperators.jl

    DeepONets, Neural Operators, Physics-Informed Neural Ops in Julia

    ...Instead of solving by finite element method, a PDE problem can be resolved by training a neural network to learn an operator mapping from infinite-dimensional space (u, t) to infinite-dimensional space f(u, t). Neural operator learns a continuous function between two continuous function spaces. The kernel can be trained on different geometry, which is learned from a graph. Fourier neural operator learns a neural operator with Dirichlet kernel to form a Fourier transformation. It performs Fourier transformation across infinite-dimensional function spaces and learns better than neural operators. ...
    Downloads: 1 This Week
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  • 5
    DynamicQuantities.jl

    DynamicQuantities.jl

    Lightweight + fast physical quantities in Julia

    DynamicQuantities defines a simple statically-typed Quantity type for Julia. Physical dimensions are stored as a value, as opposed to a parametric type, as in Unitful.jl. This can greatly improve both runtime performance, by avoiding type instabilities, and startup time, as it avoids overspecializing methods.
    Downloads: 1 This Week
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  • 6
    NonlinearSolve.jl

    NonlinearSolve.jl

    High-performance and differentiation-enabled nonlinear solvers

    Fast implementations of root-finding algorithms in Julia that satisfy the SciML common interface. For information on using the package, see the stable documentation. Use the in-development documentation for the version of the documentation that contains the unreleased features. NonlinearSolve.jl is a unified interface for the nonlinear solving packages of Julia.
    Downloads: 11 This Week
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  • 7
    DynamicHMC

    DynamicHMC

    Implementation of robust dynamic Hamiltonian Monte Carlo methods

    Implementation of robust dynamic Hamiltonian Monte Carlo methods in Julia. In contrast to frameworks that utilize a directed acyclic graph to build a posterior for a Bayesian model from small components, this package requires that you code a log-density function of the posterior in Julia. Derivatives can be provided manually, or using automatic differentiation. Consequently, this package requires that the user is comfortable with the basics of the theory of Bayesian inference, to the extent...
    Downloads: 0 This Week
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  • 8
    Roots.jl

    Roots.jl

    Root finding functions for Julia

    This package contains simple routines for finding roots, or zeros, of scalar functions of a single real variable using floating-point math. The find_zero function provides the primary interface. The basic call is find_zero(f, x0, [M], [p]; kws...) where, typically, f is a function, x0 a starting point or bracketing interval, M is used to adjust the default algorithms used, and p can be used to pass in parameters. Bisection-like algorithms. For functions where a bracketing interval is known...
    Downloads: 2 This Week
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  • 9
    MPI.jl

    MPI.jl

    MPI wrappers for Julia

    ...The syntax should look familiar if you know MPI already, but some arguments may not be needed (e.g. the type or the number of elements of arrays, which are inferred automatically), others may be placed slightly differently, and others may be optional keyword arguments (e.g. for the index of the root process, or the source and destination of point-to-point communication functions).
    Downloads: 0 This Week
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