//! Physics-Informed Denoising Diffusion Models (PIDDM)
//!
//! This crate implements Physics-Informed Diffusion Models for solving PDEs,
//! based on the approach from ICLR 2025 research on combining diffusion models
//! with physics constraints.
//!
//! # Overview
//!
//! Traditional diffusion models learn to denoise by predicting noise added to data.
//! Physics-Informed Diffusion Models add a physics residual loss during training
//! to ensure the generated samples satisfy physical constraints (e.g., PDE residuals).
//!
//! # Architecture
//!
//! ```text
//! Input noise z ~ N(0,1) → Diffusion UNet → Denoised field u
//!                               ↑
//!                     Physics loss: ||L[u] - f||²
//! ```
//!
//! # Example
//!
//! ```rust,ignore
//! use rtx_piddm::{PIDDM, DDPMScheduler, PhysicsLoss};
//!
//! // Create PIDDM model
//! let scheduler = DDPMScheduler::new(1000, 1e-4, 0.02);
//! let model = PIDDM::new(scheduler, unet, 0.1); // 0.1 = physics weight
//!
//! // Training step with physics loss
//! let loss = model.training_step(&x0, |u| physics_residual(u));
//! ```
//!
//! # References
//!
//! - Ho et al., "Denoising Diffusion Probabilistic Models" (NeurIPS 2020)
//! - Physics-Informed Neural Networks literature (Raissi et al., 2019)

#![forbid(unsafe_code)]
#![warn(missing_docs)]

mod piddm;
mod scheduler;
mod unet;

pub use piddm::{PIDDM, PIDDMConfig, PIDDMError};
pub use scheduler::{BetaSchedule, DDIMScheduler, DDPMScheduler, NoiseScheduler, SchedulerConfig};
pub use unet::{DiffusionUNet, TimeEmbedding, UNetConfig};