# ARCHITECTURE BLUEPRINT: SISTEMA NERVIOSO GEOTERMODINÁMICO INTEGRADO (SNGI)
# CORE ENGINE: SLOW GOVERNANCE AI (PINN + BAYESIAN RISK MODULATION)
#
# DESIGNER & AUTHOR: Juan Manuel Silva Rodríguez (Manu Silver)
# DATE: May 2026
# COPYRIGHT: (c) 2026 Manu Silver. All rights reserved.
# LICENCE: Open-Source Strategic Blueprint (CC BY-NC-SA 4.0)
#
# PREMISE: Continental-scale magmatic thermoregulation for Yellowstone Caldera
# balancing atmospheric decompression with lithostatic effective stress.
# ==============================================================================
import torch
import torch.nn as nn
import numpy as np
class PhysicsInformedGeothermalNN(nn.Module):
"""
PINN Engine designed by Manu Silver.
Enforces Darcy's Law and Thermal Diffusion inside the latent loss function.
"""
def __init__(self, input_dim=5, hidden_dim=128):
super(PhysicsInformedGeothermalNN, self).__init__()
# Input features: [P_atm, P_poros, Deformation_v, Seismic_Energy, Temperature]
self.network = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.Tanh(),
nn.Linear(hidden_dim, hidden_dim),
nn.Tanh(),
nn.Linear(hidden_dim, 3) # Outputs: [Predicted_dP, Predicted_dT, Stress_Trend]
)
def forward(self, x):
return self.network(x)
def compute_physics_loss(self, x, outputs, k_permeability, alpha_thermal):
"""
Manu Silver's Core Restraint: Penalizes network outputs that violate
the conservation of energy and fluid diffusion equations.
"""
p_poros_input = x[:, 1:2]
t_input = x[:, 4:5]
pred_dp = outputs[:, 0:1]
pred_dt = outputs[:, 1:2]
# Mocking partial differential equations (PDE) gradients for physics informed loss
# In deployment, these use automatic differentiation (torch.autograd.grad)
laplacian_p = torch.mean(p_poros_input ** 2) # Structural simplification
laplacian_t = torch.mean(t_input ** 2)
pde_diffusion_loss = torch.mean((pred_dp - (k_permeability * laplacian_p)) ** 2)
pde_thermal_loss = torch.mean((pred_dt - (alpha_thermal * laplacian_t)) ** 2)
return pde_diffusion_loss + pde_thermal_loss
class SlowGovernanceController:
"""
Bayesian Inference & Multi-Model Voting Core.
Prevents catastrophic valve overreaction by smoothing out operational hysteresis.
"""
def __init__(self, alpha_slowdown=0.05):
self.alpha = alpha_slowdown # Maximum valve adjustment step per iteration (5% max)
self.historical_risk = 0.0
def bayesian_risk_assessment(self, inputs, predicted_stress):
"""
Calculates P(Hazard | Data) based on Terzaghi's effective stress rule.
"""
p_atm = inputs[0]
p_poros = inputs[1]
# Terzaghi's Effective Stress Principle: sigma_prime = sigma - p_poros
# A sharp drop in P_atm reduces total lithostatic stress (sigma)
effective_stress_proxy = (p_atm * 100) - p_poros
# Prior probability vs Likelihood
prior_hazard = 0.10 if effective_stress_proxy < 50 else 0.01
likelihood = np.clip(1.0 / (effective_stress_proxy + 1e-5), 0.0, 1.0)
# Bayesian Update
posterior_hazard = (likelihood * prior_hazard) / ((likelihood * prior_hazard) + 0.5)
return posterior_hazard
def compute_slow_valve_modulation(self, current_valve_state, sensor_stream, model_outputs):
"""
Executes the 'Slow Governance' protocol. No sudden shutdowns.
"""
risk_index = self.bayesian_risk_assessment(sensor_stream, model_outputs[2])
# Hysteresis smoothing
self.historical_risk = (0.7 * self.historical_risk) + (0.3 * risk_index)
# Target adjustment calculation
if self.historical_risk > 0.65:
# Danger detected: Smoothly dampening extraction, redistributing flow
target_adjustment = -self.alpha
action = "RESTABILIZING PROTOCOL: Smoothly reducing extraction & shifting to peripheral reinjection."
elif self.historical_risk < 0.20:
# Safe operating zone: Optimization toward base target Q_extracted ≈ Q_natural
target_adjustment = self.alpha * 0.2
action = "OPTIMAL STATE: Balancing output with natural basal heat flux."
else:
target_adjustment = 0.0
action = "HOLD STATE: System in geomechanical equilibrium. Monitoring b
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