Code Documentation ================== Welcome to PINA documentation! Here you can find the modules of the package divided in different sections. The high-level structure of the package is depicted in our API. .. figure:: ../index_files/PINA_API.png :alt: PINA application program interface :align: center :width: 400 The pipeline to solve differential equations with PINA follows just five steps: 1. Define the `Problems`_ the user aim to solve 2. Generate data using built in `Geometrical Domains`_, or load high level simulation results as :doc:`LabelTensor ` 3. Choose or build one or more `Models`_ to solve the problem 4. Choose a solver across PINA available `Solvers`_, or build one using the :doc:`SolverInterface ` 5. Train the model with the PINA :doc:`Trainer `, enhance the train with `Callbacks`_ Trainer, Dataset and Datamodule -------------------------------- .. toctree:: :titlesonly: Trainer Dataset DataModule Data Types ------------ .. toctree:: :titlesonly: LabelTensor Graph LabelBatch Graphs Structures ------------------ .. toctree:: :titlesonly: GraphBuilder RadiusGraph KNNGraph Conditions ------------- .. toctree:: :titlesonly: ConditionInterface Condition DataCondition DomainEquationCondition InputEquationCondition InputTargetCondition Solvers -------------- .. toctree:: :titlesonly: SolverInterface SingleSolverInterface MultiSolverInterface SupervisedSolverInterface DeepEnsembleSolverInterface PINNInterface PINN GradientPINN CausalPINN CompetitivePINN SelfAdaptivePINN RBAPINN DeepEnsemblePINN SupervisedSolver DeepEnsembleSupervisedSolver ReducedOrderModelSolver GAROM Models ------------ .. toctree:: :titlesonly: :maxdepth: 5 FeedForward MultiFeedForward ResidualFeedForward Spline DeepONet MIONet KernelNeuralOperator FourierIntegralKernel FNO AveragingNeuralOperator LowRankNeuralOperator GraphNeuralOperator GraphNeuralKernel Blocks ------------- .. toctree:: :titlesonly: Residual Block EnhancedLinear Block Spectral Convolution Block Fourier Block Averaging Block Low Rank Block Graph Neural Operator Block Continuous Convolution Interface Continuous Convolution Block Orthogonal Block Message Passing ------------------- .. toctree:: :titlesonly: Deep Tensor Network Block E(n) Equivariant Network Block Interaction Network Block Radial Field Network Block Reduction and Embeddings -------------------------- .. toctree:: :titlesonly: Proper Orthogonal Decomposition Periodic Boundary Condition Embedding Fourier Feature Embedding Radial Basis Function Interpolation Optimizers and Schedulers -------------------------- .. toctree:: :titlesonly: Optimizer Scheduler TorchOptimizer TorchScheduler Adaptive Activation Functions ------------------------------- .. toctree:: :titlesonly: Adaptive Function Interface Adaptive ReLU Adaptive Sigmoid Adaptive Tanh Adaptive SiLU Adaptive Mish Adaptive ELU Adaptive CELU Adaptive GELU Adaptive Softmin Adaptive Softmax Adaptive SIREN Adaptive Exp Equations and Differential Operators --------------------------------------- .. toctree:: :titlesonly: EquationInterface Equation SystemEquation Equation Factory Differential Operators Problems -------------- .. toctree:: :titlesonly: AbstractProblem InverseProblem ParametricProblem SpatialProblem TimeDependentProblem Problems Zoo -------------- .. toctree:: :titlesonly: AdvectionProblem AllenCahnProblem DiffusionReactionProblem HelmholtzProblem InversePoisson2DSquareProblem Poisson2DSquareProblem SupervisedProblem Geometrical Domains -------------------- .. toctree:: :titlesonly: Domain CartesianDomain EllipsoidDomain SimplexDomain Domain Operations ------------------ .. toctree:: :titlesonly: OperationInterface Union Intersection Difference Exclusion Callbacks ----------- .. toctree:: :titlesonly: Processing callback Optimizer callback R3 Refinment callback Refinment Interface callback Weighting callback Losses and Weightings --------------------- .. toctree:: :titlesonly: LossInterface LpLoss PowerLoss WeightingInterface ScalarWeighting NeuralTangentKernelWeighting