HPFRACC

Documentation:

  • HPFRACC User Manual
    • Introduction & Features
      • Production Readiness
      • Key Features
        • ๐Ÿš€ High-Performance Engines
        • ๐Ÿง  Neural Fractional SDEs
        • ๐Ÿ“‰ Spectral Autograd Framework
        • ๐Ÿ”— Graph Neural Networks
    • Installation & Setup
      • Basic Installation
      • GPU & Machine Learning Support
      • Requirements
      • Intelligent Backend Selection
      • Verification
    • Tutorials & Examples
      • 1. Basic Calculus Operations
        • Computing a Fractional Derivative
      • 3. High-Performance Machine Learning
        • Optimized Optimizers
        • Adjoint Memory Efficiency
        • Data Caching
      • 4. Graph Neural Networks
        • Fractional Graph Convolution
    • Core Component Reference
      • 1. Fractional Operators
        • Classical Derivatives
        • Advanced Operators
        • Fractional Integrals
      • 2. Fractional Neural Networks
        • Spectral Autograd Layers
        • Stochastic & Probabilistic Layers
      • 3. Graph Neural Networks
      • 4. Neural Solver Frameworks
        • Neural fODEs & fSDEs
        • Adjoint Method
    • Science & Theory
      • 1. Scientific Applications
        • Computational Physics
        • Biophysics
      • 2. Mathematical Foundations
        • Key Definitions
      • 3. Numerical Stability & Accuracy
    • Advanced Usage & Optimization
      • 1. Configuration & Precision
      • 2. Intelligent Backend Selection
      • 3. Known Limitations & Workarounds
      • 4. Troubleshooting
    • Development & Contribution
      • 1. Reports & Analysis
      • 2. Design Documents
      • 3. Contributing
      • Internal architecture notes (Markdown)
  • Indices and tables
  • API Reference
    • Sectional API references
      • Core API Reference
        • Fractional Order Definitions
        • Core Utilities
        • Main Module
      • Derivatives and Integrals API Reference
        • Layout
        • Canonical derivative engines
        • Core Derivatives (factory and base classes)
        • Optimized Methods (aliases)
        • Integral methods (algorithms)
        • Advanced Methods
        • Special Methods
        • Fractional Implementations
        • Core Integrals
      • Solvers API Reference
        • ODE Solvers
        • PDE Solvers
        • SDE Solvers
        • Noise Models
        • Coupled graphโ€“SDE solvers
        • JAX / Diffrax utilities (optional)
      • Fractional Neural Networks API Reference
        • Architecture note
        • Native discrete fractional map (for advanced use)
        • Spectral Autograd
        • Stochastic Memory Sampling
        • Probabilistic Fractional Orders
        • Variance-Aware Training
        • GPU Optimization
        • Backend Management
        • Neural Networks
        • Neural ODEs
        • Layers
        • Tensor Operations
      • Fractional Graph Neural Networks API Reference
        • GNN Layers
        • GNN Models
      • Neural ODEs and SDEs API Reference
        • Neural ODEs
        • Neural fSDE
        • SDE Solvers
        • Noise Models
      • Special operators vs mathematical special functions
        • Fractional operator extensions
        • Mathematical special functions
    • Complete module reference
      • API Reference
        • Core Module
        • Machine Learning Module
        • Detailed API Documentation
        • Utility entry points (no duplicate autodoc stubs)
        • Configuration
        • Type Information
        • Usage Examples
        • Performance Considerations
        • Troubleshooting
  • API Reference
    • Core Module
      • Fractional Order Definitions
        • OptimizedRiemannLiouville
        • OptimizedCaputo
        • OptimizedGrunwaldLetnikov
        • optimized_riemann_liouville()
        • FractionalOrder
        • WeylDerivative
        • MarchaudDerivative
        • FractionalLaplacian
        • FractionalFourierTransform
        • RiemannLiouvilleIntegral
        • CaputoIntegral
        • CaputoFabrizioDerivative
        • AtanganaBaleanuDerivative
        • RiemannLiouville
        • RiemannLiouvilleDerivative
        • Caputo
        • CaputoDerivative
        • GrunwaldLetnikov
        • GrunwaldLetnikovDerivative
        • HadamardDerivative
        • ReizFellerDerivative
        • FractionalZTransform
        • FractionalMellinTransform
      • Core Algorithms
        • _handle_backend_failure()
        • UnifiedFractionalOperator
        • RiemannLiouville
        • Caputo
        • GrunwaldLetnikov
        • optimized_riemann_liouville()
        • optimized_caputo()
        • optimized_grunwald_letnikov()
        • OptimizedFractionalMethods
        • ParallelConfig
        • AdvancedFFTMethods
        • L1L2Schemes
        • ParallelLoadBalancer
        • ParallelOptimizedRiemannLiouville
        • ParallelOptimizedCaputo
        • ParallelOptimizedGrunwaldLetnikov
        • NumbaOptimizer
        • NumbaFractionalKernels
        • NumbaParallelManager
        • benchmark_parallel_vs_serial()
        • optimize_parallel_parameters()
        • memory_efficient_caputo()
        • block_processing_kernel()
        • GPUConfig
        • _LegacyGPUShim
        • GPUOptimizedRiemannLiouville
        • GPUOptimizedCaputo
        • GPUOptimizedGrunwaldLetnikov
        • MultiGPUManager
        • GPUOptimizedMethods
        • gpu_optimized_riemann_liouville()
        • gpu_optimized_caputo()
        • gpu_optimized_grunwald_letnikov()
        • benchmark_gpu_vs_cpu()
        • _adaptive_fft_direct_relative_l2()
        • RiemannLiouvilleIntegral
        • CaputoIntegral
        • WeylIntegral
        • riemann_liouville_integral()
        • caputo_integral()
        • optimized_riemann_liouville_integral()
        • optimized_caputo_integral()
        • WeylDerivative
        • MarchaudDerivative
        • HadamardDerivative
        • ReizFellerDerivative
        • AdomianDecomposition
        • weyl_derivative()
        • marchaud_derivative()
        • hadamard_derivative()
        • reiz_feller_derivative()
        • ParallelWeylDerivative
        • ParallelMarchaudDerivative
        • ParallelHadamardDerivative
        • ParallelReizFellerDerivative
        • ParallelAdomianDecomposition
        • OptimizedWeylDerivative
        • OptimizedMarchaudDerivative
        • OptimizedHadamardDerivative
        • OptimizedReizFellerDerivative
        • OptimizedAdomianDecomposition
        • parallel_weyl_derivative()
        • parallel_marchaud_derivative()
        • parallel_hadamard_derivative()
        • parallel_reiz_feller_derivative()
        • optimized_weyl_derivative()
        • optimized_marchaud_derivative()
        • optimized_hadamard_derivative()
        • optimized_reiz_feller_derivative()
        • _numpy_trapezoid()
        • _factorial()
        • FractionalLaplacian
        • FractionalFourierTransform
        • FractionalZTransform
        • FractionalMellinTransform
        • fractional_laplacian()
        • fractional_fourier_transform()
        • fractional_z_transform()
        • fractional_mellin_transform()
        • SpecialMethodsConfig
        • SpecialOptimizedWeylDerivative
        • SpecialOptimizedMarchaudDerivative
        • SpecialOptimizedReizFellerDerivative
        • UnifiedSpecialMethods
        • special_optimized_weyl_derivative()
        • special_optimized_marchaud_derivative()
        • special_optimized_reiz_feller_derivative()
        • unified_special_derivative()
      • Fractional Implementations
        • _AlphaCompatibilityWrapper
        • RiemannLiouvilleDerivative
        • CaputoDerivative
        • GrunwaldLetnikovDerivative
        • CaputoFabrizioDerivative
        • AtanganaBaleanuDerivative
        • FractionalLaplacian
        • FractionalFourierTransform
        • MillerRossDerivative
        • WeylDerivative
        • MarchaudDerivative
        • HadamardDerivative
        • ReizFellerDerivative
        • RieszFisherOperator
        • AdomianDecompositionMethod
        • create_fractional_integral()
        • create_riesz_fisher_operator()
        • register_fractional_implementations()
      • Core Derivatives
        • BaseFractionalDerivative
        • FractionalDerivativeOperator
        • FractionalDerivativeFactory
        • FractionalDerivativeChain
        • FractionalDerivativeProperties
        • create_fractional_derivative()
        • create_derivative_operator()
        • caputo()
        • riemann_liouville()
        • grunwald_letnikov()
      • Core Integrals
        • _handle_integration_failure()
        • FractionalIntegral
        • RiemannLiouvilleIntegral
        • CaputoIntegral
        • WeylIntegral
        • HadamardIntegral
        • create_fractional_integral()
        • analytical_fractional_integral()
        • trapezoidal_fractional_integral()
        • simpson_fractional_integral()
        • fractional_integral_properties()
        • validate_fractional_integral()
        • MillerRossIntegral
        • MarchaudIntegral
        • FractionalIntegralFactory
        • create_fractional_integral_factory()
    • Machine Learning Module
      • Fractional Autograd Framework
        • set_fft_backend()
        • get_fft_backend()
        • safe_fft()
        • safe_ifft()
        • robust_fft()
        • robust_ifft()
        • spectral_fractional_derivative()
        • fractional_derivative()
        • SpectralFractionalDerivative
        • SpectralFractionalFunction
        • SpectralFractionalLayer
        • SpectralFractionalNetwork
        • BoundedAlphaParameter
        • create_fractional_layer()
        • benchmark_backends()
        • original_set_fft_backend()
        • original_get_fft_backend()
        • original_safe_fft()
        • original_safe_ifft()
        • original_get_fractional_kernel()
        • original_spectral_fractional_derivative()
        • OriginalSpectral
        • OriginalSpectralFractionalLayer
        • OriginalSpectralFractionalNetwork
        • original_create_fractional_layer()
        • StochasticMemorySampler
        • ImportanceSampler
        • StratifiedSampler
        • ControlVariateSampler
        • stochastic_fractional_derivative()
        • StochasticFractionalLayer
        • create_stochastic_fractional_layer()
        • model()
        • guide()
        • ProbabilisticFractionalOrder
        • ProbabilisticFractionalLayer
        • create_probabilistic_fractional_layer()
        • create_normal_alpha_layer()
        • create_uniform_alpha_layer()
        • create_beta_alpha_layer()
        • VarianceMetrics
        • VarianceMonitor
        • StochasticSeedManager
        • VarianceAwareCallback
        • AdaptiveSamplingManager
        • VarianceAwareTrainer
        • create_variance_aware_trainer()
        • test_variance_aware_training()
      • GPU Optimization
        • PerformanceMetrics
        • GPUProfiler
        • ChunkedFFT
        • AMPFractionalEngine
        • GPUOptimizedSpectralEngine
        • GPUOptimizedStochasticSampler
        • gpu_optimization_context()
        • benchmark_gpu_optimization()
        • create_gpu_optimized_components()
        • test_gpu_optimization()
      • Backend Management
        • BackendType
        • BackendManager
        • get_backend_manager()
        • set_backend_manager()
        • get_active_backend()
        • switch_backend()
      • Tensor Operations
        • get_tensor_ops()
        • TensorOps
        • create_tensor()
        • switch_backend()
      • Core ML Components
        • MLConfig
        • FractionalNeuralNetwork
        • FractionalAttention
        • FractionalLossFunction
        • FractionalMSELoss
        • FractionalCrossEntropyLoss
        • FractionalAutoML
      • Neural Network Layers
        • LayerConfig
        • BackendManager
        • FractionalOps
        • FractionalLayerBase
        • FractionalConv1D
        • FractionalConv2D
        • FractionalLinear
        • FractionalLSTM
        • FractionalTransformer
        • FractionalPooling
        • FractionalBatchNorm1d
        • FractionalDropout
        • FractionalLayerNorm
        • FractionalMaxUnpool1d
        • FractionalMaxUnpool2d
        • FractionalMaxUnpool3d
        • FractionalSelfAttention
      • Graph Neural Networks
        • FractionalGraphConv
        • FractionalGraphAttention
        • FractionalGraphPooling
        • BaseFractionalGNNLayer
        • BaseFractionalGNN
        • FractionalGCN
        • FractionalGAT
        • FractionalGraphSAGE
        • FractionalGraphUNet
        • FractionalGNNFactory
      • Loss Functions
        • FractionalLossFunction
        • FractionalMSELoss
        • FractionalCrossEntropyLoss
        • FractionalHuberLoss
        • FractionalSmoothL1Loss
        • FractionalKLDivLoss
        • FractionalBCELoss
        • FractionalNLLLoss
        • FractionalPoissonNLLLoss
        • FractionalCosineEmbeddingLoss
        • FractionalMarginRankingLoss
        • FractionalMultiMarginLoss
        • FractionalTripletMarginLoss
        • FractionalCTCLoss
        • FractionalCustomLoss
        • FractionalCombinedLoss
        • FractionalSDEMSELoss
        • FractionalKLDivergenceLoss
        • FractionalPathwiseLoss
        • FractionalMomentMatchingLoss
      • Optimizers
        • FractionalOptimizer
        • OptimizedFractionalSGD
        • OptimizedFractionalAdam
        • OptimizedFractionalRMSprop
        • OptimizedBaseOptimizer
        • create_optimized_sgd()
        • create_optimized_adam()
    • Detailed API Documentation
      • Core Definitions
        • FractionalOrder
      • Core Fractional Calculus Methods
        • Canonical engines (algorithms.derivatives)
        • OptimizedRiemannLiouville
        • OptimizedCaputo
        • OptimizedGrunwaldLetnikov
        • RiemannLiouvilleDerivative
        • CaputoDerivative
        • GrunwaldLetnikovDerivative
        • Public package adapters (hpfracc.*)
      • Backend Management
        • BackendType
        • BackendManager
      • Tensor Operations
        • TensorOps
      • Neural Networks
        • FractionalNeuralNetwork
      • Graph Neural Networks
        • FractionalGCN
        • FractionalGAT
        • FractionalGraphSAGE
        • FractionalGraphUNet
        • GNN Factory
        • GNN Layers
        • FractionalGraphConv
        • FractionalGraphAttention
        • FractionalGraphPooling
        • BaseFractionalGNNLayer
      • Attention Mechanisms
      • FractionalAttention
      • Fractional Autograd Framework (spectral)
        • SpectralFractionalDerivative
        • SpectralFractionalLayer
        • SpectralFractionalNetwork
        • StochasticMemorySampler
        • ProbabilisticFractionalLayer
        • VarianceAwareTrainer
      • GPU Optimization
        • GPUProfiler
        • ChunkedFFT
        • AMPFractionalEngine
    • Utility entry points (no duplicate autodoc stubs)
      • Model Utilities
    • Configuration
      • Default Parameters
        • hpfracc.core.definitions.DEFAULT_FRACTIONAL_ORDER
        • hpfracc.ml.backends.DEFAULT_BACKEND
        • hpfracc.ml.tensor_ops.DEFAULT_DTYPE
      • Supported Backends
        • hpfracc.ml.backends.SUPPORTED_BACKENDS
      • Supported GNN Types
        • hpfracc.ml.gnn_models.SUPPORTED_GNN_TYPES
      • Supported Derivative Methods
        • hpfracc.core.derivatives.SUPPORTED_METHODS
    • Type Information
    • Usage Examples
      • Basic Fractional Calculus
      • Neural Network Usage
      • GNN Usage
      • Backend Management
      • Spectral fractional (FFT) usage
      • GPU Optimization
    • Performance Considerations
      • Backend Selection
      • Memory Management
      • Computation Optimization
    • Troubleshooting
      • Common Issues
      • Debugging Tips
  • Development Guides
    • HPFracc Development Guide
      • ๐Ÿš€ Getting Started with Development
        • Prerequisites
        • Development Setup
      • ๐Ÿ—๏ธ Project Structure
      • ๐Ÿงช Testing
        • Run All Tests
        • Run Specific Test Categories
        • Test Coverage
      • ๐Ÿ”ง Code Quality
        • Code Formatting
        • Type Checking
        • Pre-commit Hooks
      • ๐Ÿ“ฆ Building and Distribution
        • Build Package
        • Install from Source
        • PyPI Publishing
      • ๐Ÿš€ Performance Optimization
        • GPU Development
        • Parallel Computing
        • Memory Management
      • ๐Ÿ“Š Analytics and Monitoring
        • Performance Monitoring
        • Error Analysis
      • ๐Ÿ”ฌ Research and Development
        • Adding New Methods
        • Machine Learning Integration
        • Fractional Autograd Framework
      • ๐Ÿ“š Documentation
        • API Documentation
        • User Guides
        • Examples
      • ๐Ÿ› Debugging
        • Common Issues
        • Debug Tools
      • ๐Ÿค Contributing
        • Pull Request Process
        • Code Review Guidelines
      • ๐Ÿ“ˆ Release Process
        • Version Bumping
        • Release Checklist
      • ๐Ÿ” Troubleshooting
        • Development Environment Issues
        • Build Issues
        • Test Issues
      • ๐Ÿ“ž Support
    • HPFRACC development environment (summary)
      • Overview
      • Quick start
      • Core stack (from config/environment.yml)
      • Testing
      • Managing the environment
      • Troubleshooting
      • Performance
    • hpfracc.algorithms โ€” architecture, dependencies, and maintenance
      • 1. Design goals
      • 2. Layered layout (mental model)
      • 3. Dependency diagram (read-only edges)
      • 4. Naming: engines vs parallel advanced operators
      • 5. Risk register and mitigations
      • 6. Refreshing coverage (algorithms-focused)
        • Representative snapshot (2026-04-14, tests/test_algorithms/, --cov=hpfracc)
      • 7. Related documentation
    • hpfracc.special โ€” architecture, dependencies, and maintenance
      • 1. Design goals
      • 2. Module layout
      • 3. Which gamma? (API contract)
      • 4. Optional dependencies
      • 5. Upstream coupling (who imports special)
      • 6. Risks and mitigations
      • 7. Tests
      • 8. Related documentation
    • hpfracc.solvers โ€” architecture, dependencies, and maintenance
      • 1. Design goals
      • 2. Module layout (file-by-file)
      • 3. Import graph (intended edges)
      • 4. Public surface and compatibility policy (__init__.py)
      • 5. Gamma (ฮ“) usage consistency
      • 6. Naming collisions (read before mixing ecosystems)
      • 7. Optional dependencies (install surface)
      • 8. Tests (where to run what)
      • 9. Maintenance risks
      • 10. Related documentation
      • 11. Sphinx / Read the Docs
    • hpfracc.validation โ€” architecture, dependencies, and maintenance
      • 1. Design goals
      • 2. Module layout
      • 3. Dependency edges
      • 4. Naming collisions (read carefully)
      • 5. Entry points (scripts / tests)
      • 6. Risks and mitigations
      • 7. Related documentation
    • Benchmarking in hpfracc โ€” layout, coupling, and maintenance
      • 1. Three different โ€œbenchmarkโ€ surfaces
      • 2. Naming and package surface
      • 3. Dependencies and import coupling
      • 4. Outputs and risks
      • 5. Tests
      • 6. Related documentation
    • hpfracc.analytics โ€” architecture, dependencies, and maintenance
      • 1. Design goals
      • 2. Module layout (mental model)
      • 3. Dependency diagram
      • 4. Data flow (typical use)
      • 5. Naming and boundaries
      • 6. Risk register and mitigations
      • 7. Tests and coverage
      • 8. Related documentation
      • 9. Consolidation / deprecation candidates (no action required unless you choose)
    • hpfracc.utils โ€” architecture, dependencies, and maintenance
      • 1. Design goals
      • 2. Module layout
      • 3. Lazy plotting surface (__init__.py)
      • 4. Upstream coupling
      • 5. Dependencies and risks
      • 6. Tests
      • 7. Related documentation
    • HPFRACC Release Roadmap
      • Overview
      • Release Strategy
      • ๐Ÿš€ Release 1.4.0 - Core Fractional Operators & Solvers Foundation โœ… COMPLETED
        • โœ… Core Fractional Operators Implementation
        • โœ… Fractional Integrals Framework
        • โœ… Solver Framework & API Cleanup
        • โœ… Comprehensive Documentation & Examples
        • โœ… Infrastructure & Quality Assurance
      • ๐Ÿš€ Release 1.5.0 - Machine Learning Integration & Autograd Foundation โœ… COMPLETED
        • โœ… Autograd Fractional Derivatives (ML)
        • โœ… Advanced Neural Network Layers
        • โœ… Machine Learning Training Infrastructure
        • โœ… Graph Neural Networks (GNN)
        • โœ… Neural fODE Framework
        • โœ… Comprehensive ML Testing & Documentation
      • ๐Ÿš€ Release 1.6.0 - Performance Optimization & Advanced Applications ๐Ÿ”„ IN PROGRESS
        • ๐Ÿšง Performance Optimization & Benchmarking
        • ๐Ÿšง Advanced ML Components
        • ๐Ÿšง Real-World Applications
      • ๐Ÿš€ Release 1.7.0 - Extended GNN & Scientific Computing ๐Ÿ“‹ PLANNED
        • ๐Ÿ“‹ Extended Graph Neural Networks
        • ๐Ÿ“‹ Scientific Computing Integration
        • ๐Ÿ“‹ Advanced Fractional Methods
      • ๐Ÿš€ Release 1.8.0 - Uncertainty & Robustness ๐Ÿ“‹ PLANNED
        • ๐Ÿ“‹ Bayesian Neural Networks
        • ๐Ÿ“‹ Advanced Training Methods
      • ๐Ÿš€ Release 2.0.0 - Major Architecture & Performance ๐Ÿ“‹ PLANNED
        • ๐Ÿ“‹ Architectural Improvements
        • ๐Ÿ“‹ New Paradigms
      • ๐Ÿ“Š Implementation Metrics
        • Code Coverage Targets
        • Performance Targets
        • Documentation Targets
      • ๐Ÿงช Testing Strategy
        • Unit Tests
        • Integration Tests
        • Performance Tests
      • ๐Ÿ“š Documentation Strategy
        • User Documentation โœ… COMPLETED
        • Developer Documentation
        • Research Documentation โœ… COMPLETED
      • ๐Ÿ”„ Maintenance & Support
        • Bug Fixes
        • Performance Monitoring
      • ๐Ÿ“… Timeline Summary
      • ๐ŸŽฏ Success Criteria
        • Release 1.4.0 โœ… ACHIEVED
        • Release 1.5.0 โœ… ACHIEVED
        • Release 1.6.0
        • Release 2.0.0
      • ๐Ÿ† Major Achievements in Release 1.5.0
        • Technical Accomplishments
        • ML Capabilities
        • Quality Assurance
      • ๐Ÿš€ Next Phase Focus Areas
        • Immediate Priorities (Next 1-2 weeks)
        • Short-term Goals (Next 1-2 months)
        • Medium-term Vision (Next 3-6 months)
      • ๐Ÿ“Œ Execution Plan Addendum for 1.6.0: Fractional Autograd and Probabilistic Optimization
    • PyPI Setup Guide for HPFRACC
      • Overview
      • Prerequisites
      • Setup Steps
        • 1. Create PyPI API Token
        • 2. Configure Authentication
        • 3. Build and Upload
      • Security Notes
      • Troubleshooting
      • Current Status
    • Hardware Testing Plan for Realistic Data Collection
      • ๐Ÿ–ฅ๏ธ Your Hardware Setup Analysis
        • Current Machine: ASUS TUF A15 (Primary Development Machine) โญ CURRENT
        • New Gigabyte Aero X16 (Primary Testing Machine) โญ NEW
        • Lenovo ThinkPad E480 (Secondary Testing Machine)
      • ๐ŸŽฏ Realistic Multi-Hardware Testing Strategy
        • Phase 1: EEG Classification (This Week)
        • Phase 2: Multi-Hardware Performance (Next Week)
        • Phase 3: Multi-GPU Scaling (Future)
      • ๐Ÿ“Š Realistic Data Collection Plan
        • Week 1: EEG Experiments (ASUS TUF A15 - Current Machine)
        • Week 2: Multi-Hardware Performance
        • Week 3: Multi-GPU Scaling (Realistic)
      • ๐Ÿš€ Implementation Strategy
        • Immediate Actions (This Week)
        • Next Week (Multi-Hardware)
        • Future (Multi-GPU)
      • ๐Ÿ’ก Why This Approach is Perfect
        • Realistic and Honest
        • JCP Submission Ready
        • Future Research Foundation
      • ๐ŸŽฏ Expected Outcomes
        • Real EEG Results
        • Real Multi-Hardware Results
        • Realistic Multi-GPU Analysis
      • ๐Ÿ“ž Next Steps
      • ๐Ÿ”ง Technical Setup Notes
        • ASUS TUF A15 Setup (Current Machine) โญ READY
        • Gigabyte Aero X16 Setup (New Machine)
        • ThinkPad E480 Setup
        • Cloud Integration
    • Realistic Data Sources for hpfracc Research
      • ๐ŸŽฏ Goal: Get Real Data for All Categories
      • ๐Ÿง  EEG/Brain-Computer Interface Datasets
        • 1. PhysioNet EEG Motor Movement/Imagery Dataset โญ RECOMMENDED
        • 2. BCI Competition IV Dataset 2a โญ HIGHLY RECOMMENDED
        • 3. OpenNeuro Datasets
        • 4. DEAP Dataset (Emotion Analysis)
      • โšก Performance Benchmarking Datasets
        • 1. MLPerf HPC Benchmarking Datasets
        • 2. Neutrino Dataset (DeepLearnPhysics)
        • 3. MultiBench Benchmark
      • ๐Ÿ”ฌ Fractional Calculus Test Problems
        • 1. Fractional ODE Benchmark Problems
        • 2. Fractional PDE Test Cases
      • ๐Ÿ–ฅ๏ธ Multi-Hardware Performance Data
        • 1. Cloud Computing Platforms (Free Tiers)
        • 2. University Computing Resources
      • ๐Ÿ“Š Implementation Plan
        • Phase 1: EEG Classification (2-3 weeks)
        • Phase 2: Multi-Hardware Validation (1-2 weeks)
        • Phase 3: Multi-GPU Scaling (2-3 weeks)
      • ๐ŸŽฏ Immediate Actions
        • This Week:
        • Next Week:
      • ๐Ÿ’ก Benefits of Real Data
        • Scientific Integrity
        • JCP Submission
        • Future Research
      • ๐Ÿš€ Recommended Starting Point
      • ๐Ÿ“ž Next Steps
    • Realistic Multi-GPU Scaling Approach - Summary
      • ๐ŸŽฏ Problem Identified
      • โœ… Solution Implemented: Option 2 - Realistic Estimates
        • Methodology
        • Data Sources
        • Realistic Scaling Results
        • Communication Overhead Model
      • ๐Ÿ“Š Updated Manuscript Content
        • Figure Caption
        • Text Updates
      • ๐ŸŽฏ Why This Approach is Better
        • Scientific Integrity
        • JCP Submission Ready
      • ๐Ÿ“ˆ Benefits for JCP Reviewers
      • ๐Ÿš€ Next Steps for Future Work
        • Experimental Validation (Future Release)
        • Current Status
      • ๐Ÿ† Conclusion
    • Advanced Applications Summary
      • Overview
      • Working Advanced Applications
        • 1. Anomalous Diffusion in Physics
        • 2. EEG Signal Analysis (Biomedical)
        • 3. Viscoelastic Materials Modeling
        • 4. Fractional Filters (Signal Processing)
        • 5. Climate Modeling (Environmental Science)
        • 6. Fractional Convolutional Neural Networks
      • Performance Metrics Summary
      • Key Achievements
        • โœ… Real-World Applications Demonstrated
        • โœ… Performance Validation
        • โœ… Scientific Accuracy
      • Files Created/Updated
        • New Advanced Application Files
        • Results Files
      • API Compatibility
        • Working Components
        • Issues Identified
      • Manuscript Applications
        • Ready for Manuscript
        • Performance Data Available
      • Recommendations
        • For Examples
        • For Manuscript
      • Next Steps
      • Summary
    • Take a look at these files. Help me find issues, inconsistencies, implementation correctness, and how to fix them.
      • Critical Syntax Errors
        • 1. Missing Closing Bracket in ode_solvers.py
      • Major Implementation Issues
        • 2. Incomplete Method Implementations
        • 3. Inconsistent Predictor-Corrector Implementation
        • 4. Mathematically Incorrect Predictor-Corrector Formulation
      • PDE Solver Issues
        • 5. Complex and Potentially Unstable Temporal Derivative Computation
        • 6. Missing Fractional Order Validation
        • 7. Grรผnwald-Letnikov Coefficient Issues
      • Memory and Performance Issues
        • 8. Inefficient Array Operations
        • 9. Import and Dependency Issues
      • Recommended Fixes
        • Immediate Priority Fixes:
        • Algorithm Improvements:
        • Code Quality:

Deep Dives:

  • Unified Fractional Autograd Guide
    • 1. Spectral Autograd Framework
      • Key Engines
      • Basic Usage
    • 2. Stochastic Memory Sampling
      • How it Works
      • Available Samplers
    • 3. Probabilistic Fractional Orders
    • 4. Performance Tips
  • Neural Fractional SDE Guide
    • Introduction to Neural Fractional SDEs
      • What are Neural Fractional SDEs?
      • Mathematical Foundation
      • When to Use Neural fSDEs
    • Quick Start
      • Installation
      • Basic Example
      • Training Example
    • Core Concepts
      • Fractional Orders in SDEs
      • Drift and Diffusion Functions
      • Stochastic Noise Modeling
      • Memory Effects
    • Building Neural fSDE Models
      • Model Architecture Design
      • Network Configuration
      • Learnable Parameters
      • Backend Selection
    • Training with Adjoint Methods
      • Why Adjoint Methods?
      • Basic Adjoint Training
      • Memory-Efficient Checkpointing
      • Mixed Precision Training
      • Gradient Accumulation
    • Graph-SDE Coupling
      • Spatio-Temporal Dynamics
      • Coupling Mechanisms
      • Multi-Scale Modeling
      • Operator Splitting
    • Uncertainty Quantification
      • Bayesian Neural fSDEs
      • NumPyro Integration
      • Posterior Predictive Distributions
      • Confidence Intervals
    • Performance Optimization
      • Intelligent Backend Selection
      • Memory Management
      • GPU Utilization
      • Batch Processing
    • Best Practices
      • Model Selection
      • Hyperparameter Tuning
      • Numerical Stability
      • Validation Strategies
      • Debugging Tips
    • Advanced Topics
      • Learnable Fractional Orders
      • Multi-Equation Systems
      • Custom Noise Processes
    • References
    • Getting Help
  • Neural fODE Framework Guide
    • Overview
    • ๐Ÿš€ Quick Start
      • Installation
      • Basic Usage
    • ๐Ÿ—๏ธ Architecture
      • BaseNeuralODE
      • NeuralODE
      • NeuralFODE
      • NeuralODETrainer
    • ๐Ÿ”ง Configuration Options
      • Model Configuration
      • Training Configuration
    • ๐Ÿ“š Examples
      • Example 1: Simple Harmonic Oscillator
      • Example 2: Fractional Diffusion Equation
      • Example 3: Training a Neural ODE
    • ๐Ÿญ Factory Functions
      • Creating Models
      • Model Properties
    • ๐Ÿ”ฌ Research Applications
      • Physics-Informed Neural Networks (PINNs)
      • Time Series Prediction
    • โšก Performance Optimization
      • GPU Acceleration
      • Batch Processing
      • Memory Management
    • ๐Ÿงช Testing and Validation
      • Running Tests
      • Validation Examples
    • ๐Ÿ”ฎ Future Developments
      • Planned Features
      • Research Directions
    • ๐Ÿ“– References
    • ๐Ÿค Contributing
    • ๐Ÿ“ž Support
  • JAX GPU Setup for HPFRACC Library
    • Current Status
    • Installation
      • Recommended Installation for GPU Support
      • CuDNN Compatibility
    • How It Works
    • Usage
    • GPU Support Status
      • RTX 5070 (Current GPU)
      • CUDA Version Compatibility
      • Future GPU Support
    • Performance Impact
    • Troubleshooting
    • Technical Details
    • Troubleshooting CuDNN Issues
  • HPFRACC Performance Optimization Guide (v3.0.0)
    • Overview
    • Intelligent Backend Selection
      • Automatic Optimization
      • Performance Learning
    • Performance Benchmarks
      • Computational Speedup
      • Memory Efficiency
    • Optimization Strategies
      • 1. Data Size Optimization
        • Small Data (< 1K elements)
        • Medium Data (1K-100K elements)
        • Large Data (> 100K elements)
      • 2. Operation Type Optimization
        • Fractional Derivatives
        • Matrix Operations
        • FFT Operations
      • 3. Memory Management
        • Dynamic Memory Thresholds
        • Memory-Efficient Operations
      • 4. GPU Optimization
        • Multi-GPU Support
        • GPU Memory Management
      • 5. Neural Network Optimization
        • Fractional Neural Networks
        • Batch Processing
    • Performance Monitoring
      • Real-Time Performance Tracking
      • Performance Analytics
      • Backend Performance Analysis
    • Environment Configuration
      • Environment Variables
      • Programmatic Configuration
    • Best Practices
      • 1. Use Intelligent Backend Selection
      • 2. Memory Management
      • 3. Data Size Optimization
      • 4. Operation-Specific Optimization
      • 5. Neural Network Optimization
    • Troubleshooting Performance Issues
      • Common Performance Problems
        • 1. Slow Performance
        • 2. Memory Issues
        • 3. GPU Issues
      • Performance Debugging
    • Advanced Optimization Techniques
      • 1. Custom Workload Characterization
      • 2. Performance Prediction
      • 3. Adaptive Optimization
    • Conclusion
HPFRACC
  • API Reference
  • View page source

API Reference๏ƒ

Comprehensive API documentation for all HPFRACC modules, organized by functional area.

Sectional API references๏ƒ

API by area

  • Core API Reference
    • Fractional Order Definitions
    • Core Utilities
    • Main Module
  • Derivatives and Integrals API Reference
    • Layout
    • Canonical derivative engines
    • Core Derivatives (factory and base classes)
    • Optimized Methods (aliases)
    • Integral methods (algorithms)
    • Advanced Methods
    • Special Methods
    • Fractional Implementations
    • Core Integrals
  • Solvers API Reference
    • ODE Solvers
    • PDE Solvers
    • SDE Solvers
    • Noise Models
    • Coupled graphโ€“SDE solvers
    • JAX / Diffrax utilities (optional)
  • Fractional Neural Networks API Reference
    • Architecture note
    • Native discrete fractional map (for advanced use)
    • Spectral Autograd
    • Stochastic Memory Sampling
    • Probabilistic Fractional Orders
    • Variance-Aware Training
    • GPU Optimization
    • Backend Management
    • Neural Networks
    • Neural ODEs
    • Layers
    • Tensor Operations
  • Fractional Graph Neural Networks API Reference
    • GNN Layers
    • GNN Models
  • Neural ODEs and SDEs API Reference
    • Neural ODEs
    • Neural fSDE
    • SDE Solvers
    • Noise Models
  • Special operators vs mathematical special functions
    • Fractional operator extensions
    • Mathematical special functions

Complete module reference๏ƒ

Flat automodule listing (full package surface):

  • API Reference
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