home
Home
Modeling Languages
    ModelingToolkitCatalystNBodySimulatorParameterizedFunctions
Model Libraries and Importers
    ModelingToolkitStandardLibraryModelingToolkitNeuralNetsDiffEqCallbacksFiniteStateProjectionCellMLToolkitSBMLToolkitBaseModelicaReactionNetworkImporters
Symbolic Tools
    ModelOrderReductionSymbolicsSymbolicUtils
Array Libraries
    RecursiveArrayToolsComponentArraysLabelledArraysMultiScaleArrays
Equation Solvers
    LinearSolveNonlinearSolveDifferentialEquationsIntegralsDifferenceEquationsOptimizationJumpProcessesLineSearch
Inverse Problems / Estimation
    SciMLSensitivityDiffEqParamEstimDiffEqBayes
PDE Solvers
    MethodOfLinesNeuralPDENeuralOperatorsFEniCSHighDimPDEDiffEqOperators
Advanced Solver APIs
    OrdinaryDiffEqBoundaryValueDiffEqDiffEqGPU
Parameter Analysis
    EasyModelAnalysisGlobalSensitivityStructuralIdentifiability
Third-Party Parameter Analysis
    BifurcationKit
Uncertainty Quantification
    PolyChaosSciMLExpectations
Function Approximation
    SurrogatesReservoirComputing
Implicit Layer Deep Learning
    DiffEqFluxDeepEquilibriumNetworks
Symbolic Learning
    DataDrivenDiffEqSymbolicNumericIntegration
Third-Party Differentiation Tooling
    SparseDiffToolsFiniteDiff
Numerical Utilities
    ExponentialUtilitiesDiffEqNoiseProcessPreallocationToolsEllipsisNotationDataInterpolationsNDInterpolationsPoissonRandomQuasiMonteCarloRuntimeGeneratedFunctionsMuladdMacroFindFirstFunctionsSparseDiffTools
High-Level Interfaces
    SciMLBaseSciMLStructuresADTypesSymbolicIndexingInterfaceTermInterfaceSciMLOperatorsSurrogatesBaseCommonSolve
Third-Party Interfaces
    ArrayInterfaceStaticArrayInterface
Developer Documentation
    SciMLStyleColPracDiffEq Developer Documentation
Extra Resources
    SciMLWorkshopExtended SciML TutorialsThe SciML BenchmarksModelingToolkitCourse
Commercial Support
    JuliaHub logo - contact sales today!

    JuliaHub offers commercial support for ModelingToolkit and the SciML ecosystem. Contact us today to discuss your needs!
Products built with SciML
  • JuliaSim
  • Pumas
  • Cedar EDA
  • Neuroblox
  • Planting Space
    /
    NeuralPDE.jl logo
    NeuralPDE.jl
    • NeuralPDE.jl: Automatic Physics-Informed Neural Networks (PINNs)
    • ODE PINN Tutorials
      • Introduction to NeuralPDE for ODEs
      • Bayesian PINNs for Coupled ODEs
      • PINNs DAEs
      • Parameter Estimation with PINNs for ODEs
      • Improved PINNs for Inverse problems in ODEs
      • Physics informed Neural Operator ODEs
      • Deep Galerkin Method
    • PDE PINN Tutorials
      • Introduction to NeuralPDE for PDEs
      • Bayesian PINNs for PDEs
      • Using GPUs
      • Defining Systems of PDEs
      • Imposing Constraints
      • The symbolic_discretize Interface
      • Optimising Parameters (Solving Inverse Problems)
      • Solving Integro Differential Equations
      • Transfer Learning with Neural Adapter
      • The Derivative Neural Network Approximation
    • Extended Examples
      • 1D Wave Equation with Dirichlet boundary conditions
      • ODE with a 3rd-Order Derivative
      • Kuramoto–Sivashinsky equation
      • PDEs with Dependent Variables on Heterogeneous Domains
      • Linear parabolic system of PDEs
      • Nonlinear elliptic system of PDEs
      • Nonlinear hyperbolic system of PDEs
      • Complex Equations with PINNs
    • Manual
      • ODE-Specialized Physics-Informed Neural Network (PINN) Solver
      • Differential Algebraic Equation Specialized Physics-Informed Neural Solver
      • PhysicsInformedNN Discretizer for PDESystems
      • BayesianPINN Discretizer for PDESystems
      • Training Strategies
      • Adaptive Loss Functions
      • Logging Utilities
      • Transfer Learning with neural_adapter
      • Physics-Informed Neural Operator (PINO) for ODEs
    • Developer Documentation
      • Debugging PINN Solutions
    Version
    • Manual
    • Logging Utilities
    • Logging Utilities
    GitHub

    Logging Utilities

    NeuralPDE.LogOptions — Type
    LogOptions(log_frequency)
    LogOptions(; log_frequency = 50)

    Options for logging during optimization.

    source
    « Adaptive Loss FunctionsTransfer Learning with neural_adapter »

    Powered by Documenter.jl and the Julia Programming Language.

    Settings


    This document was generated with Documenter.jl version 1.11.4 on Thursday 29 May 2025. Using Julia version 1.11.5.