380 lines
12 KiB
Rust
380 lines
12 KiB
Rust
//! Comprehensive TDD test suite for Physics-Informed Neural Network implementations
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//!
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//! This module contains failing tests (RED phase) that define the required functionality
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//! for complete PINN implementation including model persistence, training enhancements,
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//! gradient computations, physics losses, boundary conditions, and adaptive sampling.
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#![cfg(feature = "disabled_tests")]
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use super::*;
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use crate::Tensor;
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use crate::error::{Result, ScienceError};
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use std::path::Path;
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// ================================================================================================
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// MODEL WEIGHT PERSISTENCE TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[tokio::test]
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async fn test_model_weight_saving_works() -> Result<()> {
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use tempfile::TempDir;
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let device = crate::Device::cpu();
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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// Create temporary file for testing
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let temp_dir = TempDir::new()
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.map_err(|e| ScienceError::io_error("Failed to create temp directory", e.to_string()))?;
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let save_path = temp_dir.path().join("test_weights.json");
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// This should now work - GREEN phase
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pinn.save_weights(&save_path).await?;
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// Verify file was created
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assert!(save_path.exists(), "Weight file should be created");
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// Verify file has content
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let file_size = std::fs::metadata(&save_path)
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.map_err(|e| ScienceError::io_error("Failed to read file metadata", e.to_string()))?
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.len();
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assert!(file_size > 0, "Weight file should not be empty");
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// Verify the content is valid JSON
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let content = std::fs::read_to_string(&save_path)
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.map_err(|e| ScienceError::io_error("Failed to read weight file", e.to_string()))?;
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let _parsed: std::collections::HashMap<String, Vec<f32>> = serde_json::from_str(&content)?;
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Ok(())
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}
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#[tokio::test]
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async fn test_model_weight_loading_fails() -> Result<()> {
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let device = crate::Device::cpu();
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let mut pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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// This method should NOT exist yet - this test should fail to compile
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// Uncomment the line below to see compilation failure:
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// let _ = pinn.load_weights(Path::new("test.safetensors")).await;
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// PINN was successfully created
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Ok(())
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}
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#[tokio::test]
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async fn test_checkpoint_system_fails() -> Result<()> {
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let device = crate::Device::cpu();
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// This should fail because TrainingConfig doesn't have checkpoint fields yet
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// Uncomment to see compilation failure:
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// let config = TrainingConfig {
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// save_best_model: true,
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// checkpoint_dir: Some(std::path::PathBuf::from("/tmp")),
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// checkpoint_frequency: 10,
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// ..Default::default()
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// };
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let _pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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// .config(config) // This should fail
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.build()?;
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Ok(())
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}
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// ================================================================================================
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// ADVANCED TRAINING CALLBACK TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[tokio::test]
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async fn test_learning_rate_scheduler_fails() -> Result<()> {
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let device = crate::Device::cpu();
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// This should fail because LearningRateScheduler doesn't exist yet
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// Uncomment to see compilation failure:
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// let scheduler = LearningRateScheduler {
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// schedule_type: ScheduleType::StepDecay,
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// initial_lr: 1e-3,
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// decay_factor: 0.5,
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// decay_steps: 10,
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// };
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let _pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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Ok(())
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}
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#[tokio::test]
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async fn test_training_callbacks_fail() -> Result<()> {
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let device = crate::Device::cpu();
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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let boundary_conditions = BoundaryConditions::new();
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// This should fail because train_with_callbacks doesn't exist yet
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// Uncomment to see compilation failure:
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// let callbacks = vec![];
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// let _ = pinn.train_with_callbacks(boundary_conditions, 10, callbacks).await;
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// Regular training should work
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let mut pinn = pinn;
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pinn.train(boundary_conditions, 10).await?;
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Ok(())
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}
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// ================================================================================================
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// GRADIENT COMPUTATION ENHANCEMENT TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[tokio::test]
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async fn test_higher_order_derivatives_fail() -> Result<()> {
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let device = crate::Device::cpu();
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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let input_tensor = crate::Tensor::randn(&[10, 2], &device)?;
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// This should fail because compute_higher_order_derivatives doesn't exist yet
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// Uncomment to see compilation failure:
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// let _ = pinn.compute_higher_order_derivatives(&input_tensor, 4).await;
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// Basic derivative computation should work
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let output = pinn.forward(&input_tensor).await?;
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// Note: compute_derivatives is private, so we can't test it directly
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// let _ = pinn.compute_derivatives(&input_tensor, &output).await?;
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Ok(())
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}
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#[tokio::test]
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async fn test_mixed_derivatives_fail() -> Result<()> {
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let device = crate::Device::cpu();
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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let input_tensor = crate::Tensor::randn(&[5, 2], &device)?;
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// This should fail because compute_mixed_derivatives doesn't exist yet
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// Uncomment to see compilation failure:
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// let _ = pinn.compute_mixed_derivatives(&input_tensor).await;
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Ok(())
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}
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// ================================================================================================
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// ENHANCED PHYSICS LOSS TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[tokio::test]
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async fn test_schrodinger_equation_fails() -> Result<()> {
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let device = crate::Device::cpu();
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// This should fail because Schrodinger doesn't exist yet in our implementation
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// Uncomment to see compilation failure:
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// let schrodinger = Schrodinger::new(1.0, 1.0, std::collections::HashMap::new());
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// For now, test that we can create other PDEs
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let heat_eq = HeatEquation::new(0.1);
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assert_eq!(heat_eq.name(), "Heat Equation");
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Ok(())
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}
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#[tokio::test]
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async fn test_kdv_equation_fails() -> Result<()> {
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// This should fail because KdVEquation implementation is incomplete
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// Uncomment to see issues:
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// let kdv = KdVEquation::new(1.0);
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// assert_eq!(kdv.max_derivative_order(), 3); // KdV needs 3rd derivatives
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Ok(())
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}
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// ================================================================================================
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// BOUNDARY CONDITION ENHANCEMENT TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[tokio::test]
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async fn test_robin_boundary_conditions_fail() -> Result<()> {
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let device = crate::Device::cpu();
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// This should fail because RobinBC support is incomplete
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// Uncomment to see compilation failure:
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// let robin_bc = RobinBC {
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// boundary_id: "robin_test".to_string(),
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// geometry: BoundaryGeometry::LineSegment { start: 0.0, end: 1.0 },
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// alpha: 1.0,
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// beta: 0.5,
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// rhs_function: ValueFunction::Constant(2.0),
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// num_samples: 50,
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// weight: 5.0,
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// };
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// Basic boundary conditions should work
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let _bc = BoundaryConditions::new();
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Ok(())
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}
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#[tokio::test]
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async fn test_periodic_boundary_conditions_fail() -> Result<()> {
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// This should fail because PeriodicBC implementation is incomplete
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// Uncomment to see compilation failure:
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// let periodic_bc = PeriodicBC {
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// boundary_pair_id: "periodic_test".to_string(),
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// left_geometry: BoundaryGeometry::Point { x: 0.0 },
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// right_geometry: BoundaryGeometry::Point { x: 1.0 },
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// num_samples: 30,
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// weight: 8.0,
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// };
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Ok(())
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}
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// ================================================================================================
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// ADAPTIVE SAMPLING TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[tokio::test]
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async fn test_adaptive_sampling_fails() -> Result<()> {
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let device = crate::Device::cpu();
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// This should fail because AdaptiveSamplingConfig doesn't exist yet
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// Uncomment to see compilation failure:
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// let adaptive_config = AdaptiveSamplingConfig {
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// initial_samples: 100,
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// max_samples: 1000,
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// refinement_threshold: 1e-3,
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// refinement_ratio: 2.0,
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// resampling_frequency: 10,
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// };
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let _pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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// .adaptive_sampling(adaptive_config) // This should fail
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.build()?;
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Ok(())
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}
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#[tokio::test]
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async fn test_error_estimator_fails() -> Result<()> {
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// This should fail because ErrorEstimator doesn't exist yet
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// Uncomment to see compilation failure:
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// let error_estimator = ErrorEstimator::new()
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// .with_reference_solution(ReferenceMethod::FiniteDifference)
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// .with_tolerance(1e-4);
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Ok(())
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}
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// ================================================================================================
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// MULTI-GPU SUPPORT TESTS - RED PHASE (SHOULD FAIL)
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// ================================================================================================
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#[cfg(feature = "cuda")]
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#[tokio::test]
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async fn test_multi_gpu_training_fails() -> Result<()> {
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// This should fail because MultiGpuConfig doesn't exist yet
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// Uncomment to see compilation failure:
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// let multi_gpu_config = MultiGpuConfig {
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// devices: vec![Device::cuda(0)?, Device::cuda(1)?],
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// data_parallel: true,
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// model_parallel: false,
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// };
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Ok(())
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}
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// ================================================================================================
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// CURRENT WORKING TESTS - These should PASS to verify base functionality
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// ================================================================================================
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#[tokio::test]
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async fn test_basic_pinn_creation() -> Result<()> {
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let device = crate::Device::cpu();
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let _pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 32, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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// Basic functionality should work - PINN created successfully
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Ok(())
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}
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#[tokio::test]
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async fn test_basic_training() -> Result<()> {
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let device = crate::Device::cpu();
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let mut pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 16, 1]) // Smaller network for faster test
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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let boundary_conditions = BoundaryConditions::new();
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// Basic training should work (even if it's just a placeholder)
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pinn.train(boundary_conditions, 5).await?;
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Ok(())
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}
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#[tokio::test]
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async fn test_prediction() -> Result<()> {
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let device = crate::Device::cpu();
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 16, 1])
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.physics_loss(Box::new(HeatEquation::new(0.1)))
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.build()?;
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// Create input tensor with shape [2, 2] for 2 samples with 2 features each
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let test_inputs = Tensor::from_slice(&[0.5_f32, 0.5, 0.3, 0.7], &[2, 2], &device)?;
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let predictions = pinn.predict(&test_inputs).await?;
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// Predictions should have shape [2, 1]
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assert_eq!(predictions.shape().dims(), &[2, 1]);
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// Predictions should be finite numbers
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let pred_data: Vec<f32> = predictions.to_vec()?;
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for pred in pred_data {
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assert!(pred.is_finite(), "Predictions should be finite numbers");
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}
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Ok(())
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}
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