Testing Guide¶
This guide covers the comprehensive testing framework for Entropic AI, including unit tests, integration tests, performance benchmarks, and validation procedures.
Testing Philosophy¶
Entropic AI testing is based on several key principles:
- Physical Consistency: Tests verify thermodynamic laws are respected
- Numerical Stability: Tests ensure algorithms work across different scales
- Reproducibility: Tests provide consistent results across runs
- Performance: Tests validate computational efficiency
- Domain Validity: Tests ensure correct behavior in application domains
Test Structure¶
tests/
├── unit/ # Unit tests for individual components
│ ├── core/ # Core thermodynamic engine tests
│ │ ├── test_energy.py
│ │ ├── test_entropy.py
│ │ ├── test_network.py
│ │ └── test_diffuser.py
│ ├── applications/ # Application-specific tests
│ │ ├── test_circuit_evolution.py
│ │ ├── test_molecule_evolution.py
│ │ └── test_law_discovery.py
│ └── utilities/ # Utility function tests
│ ├── test_preprocessing.py
│ ├── test_visualization.py
│ └── test_configuration.py
├── integration/ # Integration tests
│ ├── test_full_pipeline.py
│ ├── test_multi_objective.py
│ └── test_real_time.py
├── benchmarks/ # Performance benchmarks
│ ├── test_optimization_benchmarks.py
│ ├── test_scalability.py
│ └── test_gpu_acceleration.py
├── validation/ # Scientific validation tests
│ ├── test_thermodynamic_laws.py
│ ├── test_known_solutions.py
│ └── test_convergence_analysis.py
├── fixtures/ # Test data and configurations
│ ├── data/
│ ├── configs/
│ └── expected_results/
└── conftest.py # Pytest configuration
Unit Testing¶
Core Component Tests¶
Testing Thermodynamic Networks¶
import pytest
import torch
import numpy as np
from entropic-ai.core import ThermodynamicNetwork
from entropic-ai.core.energy import HamiltonianEnergy
from entropic-ai.core.entropy import BoltzmannEntropy
class TestThermodynamicNetwork:
"""Test thermodynamic network functionality."""
@pytest.fixture
def network(self):
"""Create test network."""
return ThermodynamicNetwork(
input_dim=10,
hidden_dims=[64, 32],
output_dim=5,
temperature=1.0
)
def test_network_initialization(self, network):
"""Test network initializes with correct parameters."""
assert network.input_dim == 10
assert network.output_dim == 5
assert network.temperature == 1.0
assert len(network.layers) == 2 # Two hidden layers
def test_forward_pass_shape(self, network):
"""Test forward pass produces correct output shape."""
batch_size = 32
input_tensor = torch.randn(batch_size, 10)
output = network(input_tensor)
assert output.shape == (batch_size, 5)
assert not torch.isnan(output).any()
assert torch.isfinite(output).all()
def test_energy_computation(self, network):
"""Test energy computation properties."""
state = torch.randn(10)
energy = network.compute_energy(state)
# Energy should be scalar
assert energy.dim() == 0
assert torch.isfinite(energy)
# Energy should be positive (with appropriate energy function)
assert energy >= 0
def test_entropy_computation(self, network):
"""Test entropy computation properties."""
state = torch.randn(10)
entropy = network.compute_entropy(state)
# Entropy should be scalar
assert entropy.dim() == 0
assert torch.isfinite(entropy)
# Entropy should be non-negative
assert entropy >= 0
def test_temperature_scaling(self, network):
"""Test network behavior at different temperatures."""
state = torch.randn(10)
temperatures = [0.1, 1.0, 10.0]
energies = []
entropies = []
for temp in temperatures:
network.set_temperature(temp)
energy = network.compute_energy(state)
entropy = network.compute_entropy(state)
energies.append(energy.item())
entropies.append(entropy.item())
# Check temperature effects
assert all(torch.isfinite(torch.tensor(energies)))
assert all(torch.isfinite(torch.tensor(entropies)))
def test_thermodynamic_consistency(self, network):
"""Test thermodynamic consistency."""
state = torch.randn(10)
# Compute thermodynamic quantities
energy = network.compute_energy(state)
entropy = network.compute_entropy(state)
temperature = network.temperature
# Free energy should be well-defined
free_energy = energy - temperature * entropy
assert torch.isfinite(free_energy)
# Test fluctuation-dissipation theorem
# <ΔE²> = kT² * C_v (heat capacity)
energy_samples = []
for _ in range(100):
perturbed_state = state + 0.01 * torch.randn_like(state)
energy_sample = network.compute_energy(perturbed_state)
energy_samples.append(energy_sample.item())
energy_variance = np.var(energy_samples)
expected_variance = temperature**2 # Simplified heat capacity
# Should be within reasonable range
assert energy_variance > 0
assert energy_variance < 10 * expected_variance
Testing Energy Functions¶
class TestEnergyFunctions:
"""Test energy function implementations."""
def test_hamiltonian_energy(self):
"""Test Hamiltonian energy computation."""
dim = 5
kinetic_operator = torch.eye(dim)
potential_function = lambda x: 0.5 * torch.sum(x**2)
hamiltonian = HamiltonianEnergy(kinetic_operator, potential_function)
# Test energy computation
state = torch.randn(dim)
energy = hamiltonian.compute_energy(state)
assert torch.isfinite(energy)
assert energy >= 0 # For this potential
def test_energy_gradient(self):
"""Test energy gradient computation."""
potential_function = lambda x: torch.sum(x**2)
state = torch.randn(10)
state.requires_grad_(True)
energy = potential_function(state)
gradient = torch.autograd.grad(energy, state)[0]
# Gradient should have same shape as state
assert gradient.shape == state.shape
# For quadratic potential, gradient should be 2*x
expected_gradient = 2 * state
torch.testing.assert_close(gradient, expected_gradient, rtol=1e-5)
def test_energy_conservation(self):
"""Test energy conservation in isolated system."""
# Create conservative system
def conservative_force(x):
return -2 * x # F = -dV/dx for V = x²
# Simulate system evolution
dt = 0.01
x = torch.tensor([1.0])
v = torch.tensor([0.0])
initial_energy = 0.5 * v**2 + x**2 # KE + PE
# Evolve system for several steps
for _ in range(100):
# Velocity Verlet integration
a = conservative_force(x)
x = x + v * dt + 0.5 * a * dt**2
a_new = conservative_force(x)
v = v + 0.5 * (a + a_new) * dt
final_energy = 0.5 * v**2 + x**2
# Energy should be conserved (within numerical precision)
torch.testing.assert_close(initial_energy, final_energy, rtol=1e-3)
Testing Entropy Measures¶
class TestEntropyMeasures:
"""Test entropy measure implementations."""
def test_shannon_entropy(self):
"""Test Shannon entropy computation."""
from entropic-ai.core.entropy import ShannonEntropy
shannon = ShannonEntropy()
# Test uniform distribution
uniform_probs = torch.ones(4) / 4
entropy = shannon.compute_entropy(uniform_probs)
expected_entropy = torch.log(torch.tensor(4.0)) # log(n) for uniform
torch.testing.assert_close(entropy, expected_entropy, rtol=1e-5)
# Test deterministic distribution
deterministic_probs = torch.tensor([1.0, 0.0, 0.0, 0.0])
entropy = shannon.compute_entropy(deterministic_probs)
assert entropy < 1e-6 # Should be nearly zero
def test_boltzmann_entropy(self):
"""Test Boltzmann entropy computation."""
from entropic-ai.core.entropy import BoltzmannEntropy
boltzmann = BoltzmannEntropy()
# Create microstate ensemble
microstates = torch.randn(1000, 10) # 1000 microstates in 10D
entropy = boltzmann.compute_entropy(microstates)
# Entropy should be positive
assert entropy > 0
assert torch.isfinite(entropy)
def test_entropy_extensivity(self):
"""Test that entropy is extensive."""
from entropic-ai.core.entropy import ShannonEntropy
shannon = ShannonEntropy()
# Two independent systems
probs1 = torch.tensor([0.5, 0.5])
probs2 = torch.tensor([0.25, 0.25, 0.25, 0.25])
entropy1 = shannon.compute_entropy(probs1)
entropy2 = shannon.compute_entropy(probs2)
# Combined system
combined_probs = torch.kron(probs1, probs2) # Tensor product
combined_entropy = shannon.compute_entropy(combined_probs)
# Should be additive for independent systems
expected_combined = entropy1 + entropy2
torch.testing.assert_close(combined_entropy, expected_combined, rtol=1e-5)
Application Tests¶
Testing Circuit Evolution¶
class TestCircuitEvolution:
"""Test circuit evolution functionality."""
@pytest.fixture
def simple_truth_table(self):
"""Simple AND gate truth table."""
return {
'inputs': ['A', 'B'],
'outputs': ['Y'],
'logic': [
(0, 0, 0),
(0, 1, 0),
(1, 0, 0),
(1, 1, 1)
]
}
def test_circuit_specification_parsing(self, simple_truth_table):
"""Test circuit specification parsing."""
from entropic-ai.applications import CircuitEvolution
evolver = CircuitEvolution()
spec = evolver.parse_truth_table(simple_truth_table)
assert spec.num_inputs == 2
assert spec.num_outputs == 1
assert len(spec.logic_rows) == 4
def test_circuit_generation(self):
"""Test random circuit generation."""
from entropic-ai.applications import CircuitEvolution
evolver = CircuitEvolution(
component_library=['AND', 'OR', 'NOT'],
max_gates=10
)
circuit = evolver.generate_random_circuit()
assert circuit is not None
assert len(circuit.gates) <= 10
assert all(gate.type in ['AND', 'OR', 'NOT'] for gate in circuit.gates)
def test_circuit_simulation(self, simple_truth_table):
"""Test circuit simulation correctness."""
from entropic-ai.applications import CircuitEvolution
from entropic-ai.circuits import Circuit, ANDGate
# Create simple AND circuit
circuit = Circuit()
and_gate = ANDGate('and1')
circuit.add_gate(and_gate)
circuit.connect_input('A', and_gate, 0)
circuit.connect_input('B', and_gate, 1)
circuit.connect_output(and_gate, 0, 'Y')
# Test simulation
test_cases = [
([0, 0], [0]),
([0, 1], [0]),
([1, 0], [0]),
([1, 1], [1])
]
for inputs, expected_outputs in test_cases:
outputs = circuit.simulate(inputs)
assert outputs == expected_outputs
@pytest.mark.slow
def test_circuit_evolution_convergence(self, simple_truth_table):
"""Test that circuit evolution converges to correct solution."""
from entropic-ai.applications import CircuitEvolution
evolver = CircuitEvolution(
component_library=['AND', 'OR', 'NOT'],
thermal_parameters={'initial_temperature': 2.0, 'cooling_rate': 0.9}
)
evolver.set_target_specification(simple_truth_table)
result = evolver.evolve(max_iterations=500)
# Should find a working circuit
assert result.final_error < 0.1
assert result.best_circuit is not None
# Verify circuit correctness
circuit = result.best_circuit
for inputs, expected_outputs in simple_truth_table['logic']:
outputs = circuit.simulate(list(inputs))
assert outputs == list(expected_outputs)
Integration Testing¶
Full Pipeline Tests¶
class TestFullPipeline:
"""Test complete optimization pipelines."""
def test_optimization_pipeline(self):
"""Test full optimization workflow."""
import torch
from entropic-ai import optimize
# Define test function (Rosenbrock)
def rosenbrock(x):
return torch.sum(100 * (x[1:] - x[:-1]**2)**2 + (1 - x[:-1])**2)
# Set up optimization
bounds = (torch.tensor([-2.0, -2.0]), torch.tensor([2.0, 2.0]))
result = optimize(
objective_function=rosenbrock,
bounds=bounds,
method='thermodynamic',
max_iterations=1000
)
# Should converge to global minimum at (1, 1)
expected_optimum = torch.tensor([1.0, 1.0])
torch.testing.assert_close(result.best_solution, expected_optimum, rtol=0.1)
assert result.final_energy < 0.1
def test_discovery_pipeline(self):
"""Test scientific discovery workflow."""
import pandas as pd
from entropic-ai import discover
# Generate synthetic pendulum data
lengths = torch.linspace(0.1, 1.0, 20)
periods = 2 * torch.pi * torch.sqrt(lengths / 9.81)
# Add small amount of noise
periods += 0.01 * torch.randn_like(periods)
data = pd.DataFrame({
'length': lengths.numpy(),
'period': periods.numpy(),
'gravity': [9.81] * len(lengths)
})
# Discover relationship
result = discover(
data=data,
target_variable='period',
method='law_discovery',
operators=['add', 'mul', 'div', 'sqrt'],
max_complexity=10
)
# Should discover relationship close to T = 2π√(L/g)
discovered_law = result.best_expression
assert discovered_law is not None
# Test law accuracy
predicted_periods = discovered_law.evaluate(data)
mse = torch.mean((torch.tensor(predicted_periods) - periods)**2)
assert mse < 0.01
Multi-Objective Integration¶
class TestMultiObjectiveIntegration:
"""Test multi-objective optimization integration."""
def test_pareto_front_generation(self):
"""Test Pareto front generation."""
from entropic-ai.optimization import MultiObjectiveOptimizer
# Define conflicting objectives
def objective1(x):
return torch.sum(x**2) # Minimize distance from origin
def objective2(x):
return torch.sum((x - 1)**2) # Minimize distance from (1,1)
optimizer = MultiObjectiveOptimizer(
objectives=[objective1, objective2],
bounds=(torch.tensor([-1.0, -1.0]), torch.tensor([2.0, 2.0]))
)
result = optimizer.evolve(max_iterations=500)
# Should generate valid Pareto front
assert len(result.pareto_front) > 0
# All solutions should be non-dominated
for i, sol1 in enumerate(result.pareto_front):
for j, sol2 in enumerate(result.pareto_front):
if i != j:
# Check that neither dominates the other
obj1_values = [obj(sol1['solution']) for obj in [objective1, objective2]]
obj2_values = [obj(sol2['solution']) for obj in [objective1, objective2]]
dominates = all(v1 <= v2 for v1, v2 in zip(obj1_values, obj2_values)) and \
any(v1 < v2 for v1, v2 in zip(obj1_values, obj2_values))
assert not dominates
Performance Benchmarks¶
Optimization Benchmarks¶
class TestOptimizationBenchmarks:
"""Benchmark optimization performance."""
@pytest.mark.benchmark
def test_sphere_function_benchmark(self, benchmark):
"""Benchmark sphere function optimization."""
from entropic-ai.optimization import ThermodynamicOptimizer
def sphere_function(x):
return torch.sum(x**2)
optimizer = ThermodynamicOptimizer(
thermal_parameters={'initial_temperature': 1.0, 'cooling_rate': 0.95}
)
bounds = (torch.tensor([-5.0] * 10), torch.tensor([5.0] * 10))
def run_optimization():
return optimizer.optimize(sphere_function, bounds, max_iterations=1000)
result = benchmark(run_optimization)
# Performance assertions
assert result.final_energy < 1e-6
assert result.convergence_iteration < 800
@pytest.mark.benchmark
def test_scalability_benchmark(self, benchmark):
"""Benchmark performance scaling with problem size."""
from entropic-ai.optimization import ThermodynamicOptimizer
results = {}
for dim in [10, 50, 100, 200]:
def sphere_function(x):
return torch.sum(x**2)
optimizer = ThermodynamicOptimizer()
bounds = (torch.tensor([-5.0] * dim), torch.tensor([5.0] * dim))
def run_optimization():
return optimizer.optimize(sphere_function, bounds, max_iterations=500)
timing_result = benchmark(run_optimization)
results[dim] = timing_result.stats['mean']
# Check that scaling is reasonable (should be roughly linear or quadratic)
time_10 = results[10]
time_100 = results[100]
# 10x increase in dimension should not be more than 100x increase in time
assert time_100 / time_10 < 100
Memory Benchmarks¶
class TestMemoryBenchmarks:
"""Benchmark memory usage."""
@pytest.mark.benchmark
def test_memory_usage_scaling(self):
"""Test memory usage with problem size."""
import psutil
import os
from entropic-ai.core import ThermodynamicNetwork
process = psutil.Process(os.getpid())
memory_usage = {}
for network_size in [100, 500, 1000, 2000]:
# Measure memory before
memory_before = process.memory_info().rss
# Create network
network = ThermodynamicNetwork(
input_dim=network_size,
hidden_dims=[network_size, network_size],
output_dim=network_size
)
# Do some operations
input_data = torch.randn(32, network_size)
output = network(input_data)
# Measure memory after
memory_after = process.memory_info().rss
memory_usage[network_size] = memory_after - memory_before
# Clean up
del network, input_data, output
torch.cuda.empty_cache() if torch.cuda.is_available() else None
# Memory usage should scale reasonably
for size in memory_usage:
# Should not use more than 10GB
assert memory_usage[size] < 10 * 1024**3
Validation Testing¶
Thermodynamic Law Validation¶
class TestThermodynamicLaws:
"""Validate adherence to thermodynamic laws."""
def test_energy_conservation(self):
"""Test first law of thermodynamics."""
from entropic-ai.core import ThermodynamicNetwork
network = ThermodynamicNetwork(10, [20], 5)
# Create isolated system evolution
initial_state = torch.randn(10)
# Track energy over time
energies = []
states = [initial_state]
current_state = initial_state
for _ in range(100):
energy = network.compute_energy(current_state)
energies.append(energy.item())
# Evolve state (in isolation, energy should be conserved)
# Use microcanonical evolution
current_state = network.evolve_microcanonical(current_state, dt=0.01)
states.append(current_state)
# Energy should be approximately conserved
energy_variance = np.var(energies)
assert energy_variance < 0.1 * np.mean(energies)
def test_entropy_increase(self):
"""Test second law of thermodynamics."""
from entropic-ai.core import ThermodynamicNetwork
network = ThermodynamicNetwork(10, [20], 5)
# Start with low-entropy (ordered) state
initial_state = torch.zeros(10)
initial_state[0] = 1.0 # Concentrated state
# Evolve in thermal contact (canonical ensemble)
entropies = []
current_state = initial_state
for _ in range(100):
entropy = network.compute_entropy(current_state)
entropies.append(entropy.item())
# Thermal evolution at fixed temperature
current_state = network.evolve_canonical(current_state, temperature=1.0, dt=0.01)
# Entropy should generally increase or remain constant
entropy_changes = np.diff(entropies)
# Allow for small fluctuations due to finite size
negative_changes = entropy_changes[entropy_changes < -0.01]
assert len(negative_changes) < 0.1 * len(entropy_changes)
def test_fluctuation_dissipation_theorem(self):
"""Test fluctuation-dissipation relationship."""
from entropic-ai.core import ThermodynamicNetwork
network = ThermodynamicNetwork(5, [10], 3)
temperature = 1.0
# Measure equilibrium fluctuations
equilibrium_energies = []
for _ in range(1000):
state = network.sample_thermal_state(temperature)
energy = network.compute_energy(state)
equilibrium_energies.append(energy.item())
energy_variance = np.var(equilibrium_energies)
# Measure response to small perturbation
reference_state = torch.randn(5)
reference_energy = network.compute_energy(reference_state)
perturbation = 0.01 * torch.randn(5)
perturbed_state = reference_state + perturbation
perturbed_energy = network.compute_energy(perturbed_state)
response = (perturbed_energy - reference_energy) / 0.01
# Fluctuation-dissipation: <ΔE²> ∝ T * response
expected_variance = temperature * abs(response.item())
# Should be within order of magnitude
assert 0.1 * expected_variance < energy_variance < 10 * expected_variance
Known Solution Validation¶
class TestKnownSolutions:
"""Test against problems with known solutions."""
def test_harmonic_oscillator(self):
"""Test harmonic oscillator energy levels."""
from entropic-ai.applications import QuantumSystemEvolution
# Set up 1D harmonic oscillator
def harmonic_potential(x):
return 0.5 * x**2
quantum_evolver = QuantumSystemEvolution(
potential_function=harmonic_potential,
dimensions=1
)
# Find ground state
ground_state = quantum_evolver.find_ground_state()
# Should find E₀ = ℏω/2 = 0.5 (with ℏ=ω=1)
expected_energy = 0.5
assert abs(ground_state.energy - expected_energy) < 0.01
# Ground state should be Gaussian
x = torch.linspace(-3, 3, 100)
psi = ground_state.wavefunction(x)
expected_psi = torch.exp(-x**2 / 2) / (torch.pi**0.25)
# Compare shapes (allow for normalization differences)
correlation = torch.corrcoef(torch.stack([psi, expected_psi]))[0, 1]
assert correlation > 0.99
def test_hydrogen_atom_ground_state(self):
"""Test hydrogen atom ground state."""
from entropic-ai.applications import AtomicSystemEvolution
# Set up hydrogen atom (simplified)
def coulomb_potential(r):
return -1.0 / (r + 1e-8) # Avoid singularity
atomic_evolver = AtomicSystemEvolution(
potential_function=coulomb_potential,
nuclear_charge=1
)
# Find ground state
ground_state = atomic_evolver.find_ground_state()
# Should find E₀ = -13.6 eV = -0.5 Hartree
expected_energy = -0.5
assert abs(ground_state.energy - expected_energy) < 0.05
def test_traveling_salesman_optimal(self):
"""Test TSP with known optimal solution."""
from entropic-ai.applications import CombinatorialOptimization
import networkx as nx
# Create small TSP with known solution
# 4-city square: optimal tour length = 4
graph = nx.Graph()
cities = [(0, 0), (1, 0), (1, 1), (0, 1)]
for i, pos1 in enumerate(cities):
for j, pos2 in enumerate(cities):
if i < j:
distance = np.sqrt((pos1[0] - pos2[0])**2 + (pos1[1] - pos2[1])**2)
graph.add_edge(i, j, weight=distance)
tsp_optimizer = CombinatorialOptimization(
problem_graph=graph,
problem_type='tsp'
)
result = tsp_optimizer.evolve(max_iterations=1000)
# Should find optimal tour length = 4
expected_length = 4.0
assert abs(result.best_objective - expected_length) < 0.1
Test Configuration¶
Pytest Configuration¶
# conftest.py
import pytest
import torch
import numpy as np
import os
def pytest_configure(config):
"""Configure pytest environment."""
# Set random seeds for reproducibility
torch.manual_seed(42)
np.random.seed(42)
# Configure torch settings
torch.set_default_dtype(torch.float32)
# Set environment variables
os.environ['ENTROPIC_AI_TEST_MODE'] = '1'
@pytest.fixture(scope="session")
def device():
"""Get device for testing."""
return torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@pytest.fixture
def temp_directory(tmp_path):
"""Create temporary directory for test files."""
return tmp_path
@pytest.fixture
def sample_data():
"""Generate sample test data."""
torch.manual_seed(123)
return {
'input_data': torch.randn(100, 10),
'target_data': torch.randn(100, 5),
'labels': torch.randint(0, 3, (100,))
}
# Markers for different test types
pytest.mark.slow = pytest.mark.skipif(
not pytest.config.getoption("--run-slow"),
reason="need --run-slow option to run"
)
pytest.mark.gpu = pytest.mark.skipif(
not torch.cuda.is_available(),
reason="GPU not available"
)
pytest.mark.benchmark = pytest.mark.skipif(
not pytest.config.getoption("--benchmark-only"),
reason="need --benchmark-only option to run"
)
Test Execution¶
# Run all tests
pytest
# Run with coverage
pytest --cov=entropic-ai --cov-report=html
# Run specific test categories
pytest -m "not slow" # Skip slow tests
pytest -m "gpu" # Run only GPU tests
pytest -m "benchmark" # Run only benchmarks
# Run tests in parallel
pytest -n auto
# Run with verbose output
pytest -v
# Run specific test file
pytest tests/unit/core/test_thermodynamic_network.py
# Run specific test function
pytest tests/unit/core/test_thermodynamic_network.py::TestThermodynamicNetwork::test_energy_computation
Continuous Integration¶
# .github/workflows/tests.yml
name: Tests
on: [push, pull_request]
jobs:
test:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: [3.9, 3.10, 3.11]
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
pip install -e ".[test]"
- name: Run unit tests
run: |
pytest tests/unit/ --cov=entropic-ai --cov-report=xml
- name: Run integration tests
run: |
pytest tests/integration/ -v
- name: Upload coverage
uses: codecov/codecov-action@v1
with:
file: ./coverage.xml
Testing Best Practices¶
Test Design Principles¶
- Independence: Tests should not depend on each other
- Reproducibility: Tests should give consistent results
- Fast Execution: Unit tests should run quickly
- Clear Assertions: Test failures should be easy to diagnose
- Edge Cases: Test boundary conditions and error cases
Performance Testing¶
- Benchmarking: Use consistent hardware and conditions
- Profiling: Identify performance bottlenecks
- Scaling: Test performance across different problem sizes
- Memory: Monitor memory usage and leaks
Scientific Validation¶
- Physical Laws: Verify thermodynamic consistency
- Known Solutions: Test against analytical solutions
- Convergence: Verify algorithm convergence properties
- Numerical Stability: Test across different numerical scales
This comprehensive testing framework ensures that Entropic AI maintains high quality, performance, and scientific validity across all components and applications.