Applications API Reference¶
This module provides high-level application interfaces for common use cases of Entropic AI. The applications package includes pre-built solvers for optimization, design, discovery, and generation tasks across various domains.
Core Application Classes¶
BaseApplication¶
The foundation class for all Entropic AI applications.
class BaseApplication:
"""Base class for all Entropic AI applications.
Provides common functionality and interfaces for thermodynamic
evolution-based problem solving.
"""
def __init__(self, config: ApplicationConfig):
"""Initialize base application.
Args:
config: Application configuration containing:
- thermal_parameters: Temperature and cooling settings
- complexity_constraints: Complexity optimization settings
- domain_specific: Domain-specific parameters
"""
self.config = config
self.thermal_network = None
self.complexity_optimizer = None
self.evolution_state = None
def setup(self, problem_definition: ProblemDefinition) -> None:
"""Setup application for specific problem.
Args:
problem_definition: Problem-specific configuration including:
- objective_function: Function to optimize
- constraints: Problem constraints
- variable_bounds: Variable boundaries
- evaluation_metrics: Success metrics
"""
def evolve(self,
initial_state: Optional[torch.Tensor] = None,
max_iterations: int = 1000,
convergence_threshold: float = 1e-6) -> EvolutionResult:
"""Run thermodynamic evolution.
Args:
initial_state: Starting point for evolution (random if None)
max_iterations: Maximum number of evolution steps
convergence_threshold: Convergence criteria
Returns:
EvolutionResult containing:
- best_solution: Optimal solution found
- final_energy: Final energy value
- evolution_history: Complete evolution trace
- convergence_info: Convergence statistics
"""
def evaluate_solution(self, solution: torch.Tensor) -> Dict[str, float]:
"""Evaluate solution quality.
Args:
solution: Solution to evaluate
Returns:
Dictionary of evaluation metrics
"""
Optimization Applications¶
ContinuousOptimization¶
Solve continuous optimization problems using thermodynamic evolution.
class ContinuousOptimization(BaseApplication):
"""Continuous optimization using thermodynamic principles.
Suitable for:
- Non-linear optimization
- Multi-modal landscapes
- Constrained optimization
- Global optimization
"""
def __init__(self,
objective_function: Callable[[torch.Tensor], torch.Tensor],
bounds: Tuple[torch.Tensor, torch.Tensor],
constraints: Optional[List[Constraint]] = None):
"""Initialize continuous optimizer.
Args:
objective_function: Function to minimize f(x) -> scalar
bounds: (lower_bounds, upper_bounds) for variables
constraints: List of constraint objects
"""
def add_constraint(self, constraint: Constraint) -> None:
"""Add optimization constraint.
Args:
constraint: Constraint object with methods:
- evaluate(x): Returns constraint violation
- gradient(x): Returns constraint gradient
"""
def set_cooling_schedule(self, schedule: CoolingSchedule) -> None:
"""Set temperature cooling schedule.
Args:
schedule: Cooling schedule object with:
- initial_temperature: Starting temperature
- final_temperature: Ending temperature
- cooling_rate: Rate of temperature decrease
- schedule_type: 'exponential', 'linear', 'adaptive'
"""
# Example usage
optimizer = ContinuousOptimization(
objective_function=lambda x: torch.sum(x**2), # Sphere function
bounds=(torch.tensor([-5.0] * 10), torch.tensor([5.0] * 10))
)
result = optimizer.evolve(max_iterations=2000)
print(f"Optimal solution: {result.best_solution}")
print(f"Optimal value: {result.final_energy}")
CombinatorialOptimization¶
Solve discrete optimization problems.
class CombinatorialOptimization(BaseApplication):
"""Combinatorial optimization using thermodynamic evolution.
Suitable for:
- Traveling salesman problems
- Graph coloring
- Scheduling problems
- Assignment problems
"""
def __init__(self,
problem_graph: nx.Graph,
objective_type: str = 'minimize',
neighborhood_function: Optional[Callable] = None):
"""Initialize combinatorial optimizer.
Args:
problem_graph: NetworkX graph representing problem structure
objective_type: 'minimize' or 'maximize'
neighborhood_function: Function defining solution neighborhoods
"""
def set_encoding(self, encoding: DiscreteEncoding) -> None:
"""Set solution encoding scheme.
Args:
encoding: Encoding object that handles:
- encode(solution): Convert to thermodynamic representation
- decode(state): Convert from thermodynamic representation
- validate(solution): Check solution validity
"""
def add_local_search(self, local_search: LocalSearch) -> None:
"""Add local search component.
Args:
local_search: Local search object for solution refinement
"""
# Example: Traveling Salesman Problem
tsp_graph = nx.complete_graph(20) # 20-city TSP
for (u, v) in tsp_graph.edges():
tsp_graph[u][v]['weight'] = np.random.uniform(1, 10)
tsp_optimizer = CombinatorialOptimization(
problem_graph=tsp_graph,
objective_type='minimize'
)
tsp_result = tsp_optimizer.evolve()
Design Applications¶
CircuitEvolution¶
Evolve digital circuit designs using thermodynamic principles.
class CircuitEvolution(BaseApplication):
"""Digital circuit synthesis and optimization.
Features:
- Logic synthesis from truth tables
- Multi-objective optimization (area, power, delay)
- Technology mapping
- Noise resilience optimization
"""
def __init__(self,
component_library: ComponentLibrary,
technology_node: str = '14nm',
optimization_objectives: List[str] = ['area', 'power', 'delay']):
"""Initialize circuit evolution.
Args:
component_library: Available logic gates and components
technology_node: Target technology node
optimization_objectives: List of objectives to optimize
"""
def set_specification(self, spec: CircuitSpecification) -> None:
"""Set circuit design specification.
Args:
spec: Circuit specification containing:
- truth_table: Desired logic function
- timing_constraints: Performance requirements
- power_budget: Power consumption limits
- area_constraints: Size limitations
"""
def add_noise_model(self, noise_model: NoiseModel) -> None:
"""Add noise model for robust design.
Args:
noise_model: Thermal and process noise model
"""
def synthesize(self) -> CircuitDesign:
"""Synthesize circuit design.
Returns:
CircuitDesign object containing:
- netlist: Circuit netlist
- performance_metrics: Area, power, delay
- verification_results: Correctness verification
"""
# Example: 4-bit adder synthesis
adder_spec = CircuitSpecification(
truth_table=generate_adder_truth_table(4),
timing_constraint=TimeConstraint(max_delay=2.0), # ns
power_budget=PowerBudget(max_power=10.0), # mW
area_constraint=AreaConstraint(max_area=1000.0) # μm²
)
circuit_evolver = CircuitEvolution(
component_library=StandardCellLibrary('14nm'),
optimization_objectives=['area', 'power', 'delay']
)
circuit_evolver.set_specification(adder_spec)
design = circuit_evolver.synthesize()
ArchitecturalDesign¶
Optimize architectural and structural designs.
class ArchitecturalDesign(BaseApplication):
"""Architectural design optimization.
Applications:
- Building layout optimization
- Structural design
- Network topology design
- System architecture optimization
"""
def __init__(self,
design_space: DesignSpace,
structural_constraints: List[StructuralConstraint],
performance_objectives: List[str]):
"""Initialize architectural design.
Args:
design_space: Definition of design variable space
structural_constraints: Physical and code constraints
performance_objectives: Performance metrics to optimize
"""
def add_building_codes(self, codes: BuildingCodes) -> None:
"""Add building code constraints.
Args:
codes: Building code specifications
"""
def set_environmental_conditions(self, conditions: EnvironmentalConditions) -> None:
"""Set environmental design conditions.
Args:
conditions: Environmental parameters (wind, seismic, thermal)
"""
def optimize_design(self) -> ArchitecturalSolution:
"""Optimize architectural design.
Returns:
ArchitecturalSolution with optimized parameters
"""
Discovery Applications¶
LawDiscovery¶
Discover scientific laws and mathematical relationships from data.
class LawDiscovery(BaseApplication):
"""Scientific law discovery from experimental data.
Capabilities:
- Symbolic regression
- Physical law discovery
- Mathematical relationship extraction
- Multi-scale law discovery
"""
def __init__(self,
operator_library: List[str],
complexity_weights: Dict[str, float],
dimensional_analysis: bool = True):
"""Initialize law discovery.
Args:
operator_library: Available mathematical operators
complexity_weights: Weights for different complexity measures
dimensional_analysis: Enable dimensional consistency checking
"""
def set_data(self,
data: pd.DataFrame,
variable_context: VariableContext) -> None:
"""Set experimental data and variable context.
Args:
data: Experimental data with variables as columns
variable_context: Variable metadata including:
- units: Physical units for each variable
- types: Variable types (independent/dependent)
- constraints: Variable constraints
"""
def add_physics_constraints(self, constraints: List[PhysicsConstraint]) -> None:
"""Add physics-based constraints.
Args:
constraints: List of physical principles to enforce
"""
def discover_laws(self,
target_variables: List[str],
max_complexity: int = 20) -> List[SymbolicExpression]:
"""Discover laws for target variables.
Args:
target_variables: Variables to find laws for
max_complexity: Maximum allowed expression complexity
Returns:
List of discovered symbolic expressions
"""
# Example: Discover pendulum law
pendulum_data = pd.DataFrame({
'length': [0.1, 0.2, 0.3, 0.4, 0.5],
'period': [0.63, 0.89, 1.10, 1.27, 1.42],
'mass': [0.1] * 5,
'gravity': [9.81] * 5
})
variable_context = VariableContext(
units={'length': 'm', 'period': 's', 'mass': 'kg', 'gravity': 'm/s²'},
independent=['length', 'mass', 'gravity'],
dependent=['period']
)
law_discoverer = LawDiscovery(
operator_library=['add', 'mul', 'div', 'pow', 'sqrt'],
dimensional_analysis=True
)
law_discoverer.set_data(pendulum_data, variable_context)
discovered_laws = law_discoverer.discover_laws(['period'])
PatternDiscovery¶
Discover patterns in complex datasets.
class PatternDiscovery(BaseApplication):
"""Pattern discovery in high-dimensional data.
Techniques:
- Anomaly detection
- Clustering with thermodynamic principles
- Feature selection
- Causal relationship discovery
"""
def __init__(self,
pattern_types: List[str],
complexity_regularization: float = 0.1):
"""Initialize pattern discovery.
Args:
pattern_types: Types of patterns to discover
complexity_regularization: Regularization strength
"""
def discover_clusters(self,
data: torch.Tensor,
num_clusters: Optional[int] = None) -> ClusteringResult:
"""Discover clusters using thermodynamic principles.
Args:
data: Input data tensor
num_clusters: Number of clusters (auto-detected if None)
Returns:
ClusteringResult with cluster assignments and centers
"""
def detect_anomalies(self, data: torch.Tensor) -> AnomalyResult:
"""Detect anomalies using entropy-based methods.
Args:
data: Input data tensor
Returns:
AnomalyResult with anomaly scores and detections
"""
Generation Applications¶
MoleculeEvolution¶
Generate and optimize molecular structures.
class MoleculeEvolution(BaseApplication):
"""Molecular structure generation and optimization.
Applications:
- Drug discovery
- Material design
- Catalyst optimization
- Protein folding prediction
"""
def __init__(self,
element_library: List[str],
bond_constraints: BondConstraints,
target_properties: Dict[str, float]):
"""Initialize molecule evolution.
Args:
element_library: Available chemical elements
bond_constraints: Chemical bonding rules
target_properties: Desired molecular properties
"""
def set_property_predictors(self, predictors: Dict[str, PropertyPredictor]) -> None:
"""Set molecular property prediction models.
Args:
predictors: Dictionary of property prediction models
"""
def evolve_molecule(self,
starting_structure: Optional[Molecule] = None) -> MoleculeResult:
"""Evolve molecular structure.
Args:
starting_structure: Initial molecular structure
Returns:
MoleculeResult with optimized structure and properties
"""
# Example: Drug-like molecule generation
molecule_evolver = MoleculeEvolution(
element_library=['C', 'N', 'O', 'H', 'S', 'P'],
bond_constraints=DrugLikeBondConstraints(),
target_properties={
'molecular_weight': (200, 500), # Da
'logP': (0, 5), # Lipophilicity
'TPSA': (0, 140), # Topological polar surface area
'binding_affinity': 'maximize'
}
)
drug_result = molecule_evolver.evolve_molecule()
ContentGeneration¶
Generate creative content using thermodynamic evolution.
class ContentGeneration(BaseApplication):
"""Creative content generation.
Content types:
- Text generation
- Image generation
- Music composition
- Code generation
"""
def __init__(self,
content_type: str,
style_constraints: StyleConstraints,
quality_metrics: List[str]):
"""Initialize content generation.
Args:
content_type: Type of content to generate
style_constraints: Style and format constraints
quality_metrics: Quality assessment metrics
"""
def generate_content(self,
prompt: str,
length_target: int,
creativity_level: float = 0.7) -> GeneratedContent:
"""Generate content from prompt.
Args:
prompt: Input prompt or seed
length_target: Target content length
creativity_level: Balance between coherence and novelty
Returns:
GeneratedContent with text/image/music and quality scores
"""
Utility Functions¶
Evolution Monitoring¶
class EvolutionMonitor:
"""Monitor and visualize evolution progress."""
def __init__(self, metrics: List[str]):
"""Initialize evolution monitor.
Args:
metrics: List of metrics to track
"""
def start_monitoring(self, evolution_process: BaseApplication) -> None:
"""Start monitoring evolution process."""
def plot_evolution_trace(self,
save_path: Optional[str] = None) -> matplotlib.figure.Figure:
"""Plot evolution progress."""
def export_evolution_data(self, format: str = 'csv') -> str:
"""Export evolution data."""
def visualize_energy_landscape(application: BaseApplication,
variable_ranges: Dict[str, Tuple[float, float]],
resolution: int = 50) -> matplotlib.figure.Figure:
"""Visualize energy landscape for 2D problems.
Args:
application: Configured application instance
variable_ranges: Ranges for visualization variables
resolution: Grid resolution for visualization
Returns:
Matplotlib figure with energy landscape
"""
def benchmark_performance(applications: List[BaseApplication],
test_problems: List[TestProblem],
metrics: List[str]) -> pd.DataFrame:
"""Benchmark application performance.
Args:
applications: List of applications to benchmark
test_problems: List of test problems
metrics: Performance metrics to evaluate
Returns:
DataFrame with benchmark results
"""
Configuration Helpers¶
def create_optimization_config(problem_type: str,
difficulty_level: str = 'medium') -> ApplicationConfig:
"""Create standard optimization configuration.
Args:
problem_type: Type of optimization problem
difficulty_level: 'easy', 'medium', 'hard'
Returns:
Pre-configured ApplicationConfig
"""
def create_discovery_config(domain: str,
data_size: int) -> ApplicationConfig:
"""Create standard discovery configuration.
Args:
domain: Scientific domain (physics, biology, etc.)
data_size: Size of dataset
Returns:
Pre-configured ApplicationConfig
"""
def load_application_from_config(config_path: str) -> BaseApplication:
"""Load application from configuration file.
Args:
config_path: Path to configuration file
Returns:
Configured application instance
"""
Error Handling¶
All application classes include comprehensive error handling:
class EntropicAIError(Exception):
"""Base exception for Entropic AI applications."""
pass
class ConvergenceError(EntropicAIError):
"""Raised when evolution fails to converge."""
pass
class ConfigurationError(EntropicAIError):
"""Raised for invalid configurations."""
pass
class DimensionalityError(EntropicAIError):
"""Raised for dimensional consistency violations."""
pass
Performance Considerations¶
- GPU Acceleration: All applications support CUDA acceleration
- Parallel Processing: Multi-core processing for population-based methods
- Memory Management: Efficient memory usage for large-scale problems
- Checkpointing: Save and resume long-running evolution processes
Examples Repository¶
Complete examples for each application are available in the examples/ directory:
examples/optimization/- Optimization problem examplesexamples/design/- Design optimization examplesexamples/discovery/- Scientific discovery examplesexamples/generation/- Generative application examples
Each example includes: - Problem setup code - Configuration files - Expected results - Performance benchmarks - Visualization code