Expand description
Domain-specific simulation engines.
Each domain implements a specific type of simulation:
- Physics: Rigid body, orbital, fluid dynamics
- Monte Carlo: Stochastic sampling with variance reduction
- Optimization: Bayesian, evolutionary, gradient-based
- ML: Training simulation, prediction, multi-turn evaluation
Re-exports§
pub use ml::AnomalyDetector;pub use ml::AnomalyPattern;pub use ml::AnomalyType;pub use ml::InferenceConfig;pub use ml::JidokaMLFeedback;pub use ml::MultiTurnEvaluation;pub use ml::MultiTurnSimulation;pub use ml::ParetoAnalysis;pub use ml::ParetoPoint;pub use ml::PredictionSimulation;pub use ml::PredictionState;pub use ml::RollingStats;pub use ml::RulePatch;pub use ml::RuleType;pub use ml::TrainingAnomaly;pub use ml::TrainingConfig;pub use ml::TrainingMetrics;pub use ml::TrainingSimulation;pub use ml::TrainingState;pub use ml::TrainingTrajectory;pub use ml::Turn;pub use ml::TurnMetrics;pub use monte_carlo::MonteCarloEngine;pub use monte_carlo::MonteCarloResult;pub use monte_carlo::VarianceReduction;pub use optimization::AcquisitionFunction;pub use optimization::BayesianOptimizer;pub use optimization::GaussianProcess;pub use optimization::OptimizerConfig;pub use physics::EulerIntegrator;pub use physics::Integrator;pub use physics::PhysicsEngine;pub use physics::RK4Integrator;pub use physics::VerletIntegrator;
Modules§
- ml
- Machine Learning Simulation Engine.
- monte_
carlo - Monte Carlo simulation engine.
- optimization
- Optimization engine with Bayesian optimization.
- physics
- Physics domain engine.