//! # rtx-neuro-pinn
//!
//! Physics-Informed Neural Networks for MEG/EEG source localization.
//!
//! This crate provides PINN-based solutions to the MEG/EEG inverse problem,
//! using bioelectromagnetic physics constraints to improve source estimation.
//!
//! ## Key Features
//!
//! - **Maxwell Residuals**: Quasi-static Maxwell equations for neural currents
//! - **Learnable Conductivity**: Joint estimation of tissue conductivity and sources
//! - **Physics Constraints**: Divergence-free current enforcement
//! - **Multi-Scale**: Handles arbitrary head geometries
//!
//! ## Physics Model
//!
//! The quasi-static Maxwell equations for neural currents:
//!
//! ```text
//! ∇·(σ∇Φ) = ∇·Jp
//!
//! where:
//!   Φ(r) = electric potential at position r
//!   σ(r) = tissue conductivity (can be learned)
//!   Jp(r) = primary current density (neural sources)
//! ```
//!
//! ## Example
//!
//! ```ignore
//! use rtx_neuro_pinn::{SourcePINN, SourcePINNConfig, HeadModel};
//!
//! // Create head model with tissue layers
//! let head = HeadModel::spherical(3)
//!     .with_conductivity("brain", 0.33)
//!     .with_conductivity("skull", 0.01)
//!     .with_conductivity("scalp", 0.43);
//!
//! // Create PINN solver
//! let mut pinn = SourcePINN::new(SourcePINNConfig {
//!     hidden_layers: vec![64, 128, 64],
//!     learn_conductivity: true,
//!     ..Default::default()
//! })?;
//!
//! // Train with sensor measurements
//! pinn.train(&sensor_data, &head, 1000)?;
//!
//! // Estimate sources
//! let sources = pinn.estimate_sources(&sensor_data)?;
//! ```

#![warn(missing_docs)]

pub mod conductivity;
pub mod error;
pub mod head_model;
pub mod maxwell;
pub mod network;
pub mod solver;

// Re-export main types
pub use conductivity::{ConductivityModel, LearnableConductivity, TissueLayer};
pub use error::{PinnError, PinnResult};
pub use head_model::{HeadGeometry, HeadModel, SensorArray};
pub use maxwell::{CurrentDensity, MaxwellResidual, QuasiStaticMaxwell};
pub use network::{FourierFeatures, SourceNetwork, SourceNetworkConfig};
pub use solver::{SourceEstimate, SourcePINN, SourcePINNConfig, TrainingResult};

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_basic_head_model() {
        let head = HeadModel::spherical(3);
        assert_eq!(head.n_layers(), 3);
    }

    #[test]
    fn test_maxwell_residual() {
        // Test that residual computation works
        let residual = QuasiStaticMaxwell::new(0.33);
        assert!(residual.conductivity() > 0.0);
    }
}