Agentic AI-driven modulation of electrolyzer component segmentation (via multi-modal deep learning) will reveal coalition-based equilibrium deviations in decentralized green hydrogen markets by dynamically adjusting hydrogen production rates in response to real-time material degradation patterns, thereby optimizing both physical asset longevity and market trustworthiness metrics.

A multi-modal deep learning system that segments electrolyzer component degradation from sensor data (thermal imaging, electrochemical impedance spectroscopy, current/voltage traces) and feeds this into an agentic control loop that adjusts production setpoints will, when deployed across a simulated or real network of ≥5 decentralized electrolyzer nodes, (a) reduce component degradation rate (measured as capacity fade % per 1000 operating hours) by ≥15% versus static/rule-based control, and (b) reduce hydrogen price volatility (measured as coefficient of variation of clearing price over a 90-day simulated market period) by ≥10% versus a no-AI baseline, with both effects statistically significant at p<0.05 across ≥30 independent trials/seeds.

Phase 0 (Simulation-only MVP, days 1-30): Build a digital twin combining (a) a physics-informed degradation model (e.g., PEM catalyst/membrane degradation ODEs calibrated to published NREL/DOE datasets) and (b) an agent-based market simulator (e.g., Mesa/PyMarket framework) with 5-20 electrolyzer-node agents. Train multi-modal segmentation network on synthetic + any available public degradation datasets. Run controlled A/B: AI-modulated control vs. static PID/rule-based control, across 30+ Monte Carlo market seeds, 90 simulated days each. Phase 1 (Bench validation, days 31-75, optional/stretch): Deploy on 1-2 physical lab-scale PEM electrolyzer test stands (if partner lab available) instrumented with thermal camera + EIS + V/I logging, running 500-1000 hour accelerated degradation test comparing AI-adaptive setpoint control vs. fixed setpoint. Phase 2 (Market coupling test, days 76-90): Couple bench/simulated degradation outputs into a live agent-based market simulation with injected "coalition" scenarios (colluding subset of producer agents) to test whether the pipeline detects equilibrium deviations.

# Phase 0: Simulation MVP
class DegradationSimulator:
    def generate(self, n_devices, hours) -> MultiModalTimeSeries:
        # physics-informed ODE for membrane/catalyst degradation
        # outputs: thermal_map, EIS_spectrum, V_I_trace, ground_truth_state

class SegmentationModel(nn.Module):
    # late-fusion: CNN branch (thermal) + Transformer branch (EIS, V/I sequences)
    def forward(self, thermal, eis, vi) -> degradation_state_logits

class AgenticController:
    def __init__(self, policy_type="rule_based"|"RL"):
        ...
    def act(self, degradation_state, market_signal) -> production_setpoint

class HydrogenMarketSim:
    def __init__(self, n_agents, price_mechanism="auction"):
        self.agents = [ProducerAgent(controller=AgenticController()) for _ in range(n_agents)]
    def step(self):
        for agent in self.agents:
            state = SegmentationModel(agent.sensor_stream)
            setpoint = agent.controller.act(state, self.current_price)
            agent.produce(setpoint)
        self.clear_market()
    def inject_coalition(self, agent_subset):
        # coordinate bids among subset to test equilibrium deviation detection

# Main experiment loop
for seed in range(30):
    for condition in ["static", "AI_modulated"]:
        sim = HydrogenMarketSim(n_agents=10)
        run_90_day_simulation(sim, condition, seed)
        log_metrics(degradation_rate, price_cv)

statistical_comparison(logs, test="mann_whitney", correction="bonferroni")