The use of artificial intelligence technology to enhance the stealth capabilities and combat effectiveness of stealth ships is a cutting-edge field involving multiple disciplines. The following suggestions are made in different directions from the perspective of technical feasibility and military application： ###1. Enhanced stealth ability 1. **Dynamic shape optimization system** -Analyze the angle of incidence and frequency characteristics of radar waves in real time through AI, control the deformable module on the surface of the ship (such as intelligent skin), and dynamically adjust the shape of the reflective surface. -Application case: DARPA's "Programmable Materials" project has realized dynamic scattering regulation in the millimeter band. 2. **Intelligent wave-absorbing material management system** -Based on the deep learning electromagnetic characteristic prediction model, the impedance matching of multi-layer wave-absorbing materials is automatically adjusted. -Experimental data: The AI material design system developed by MIT can reduce the reflectance of specific frequency bands by 15dB. 3. **Multiphysics stealth collaboration** -Combining electromagnetic/infrared/acoustic sensor data, AI calculates the optimal stealth strategy (such as the timing of the start and stop of the cooling system, and the adjustment of the propeller speed). ### 2. Improve combat effectiveness 1. **Cognitive electronic warfare system** - Using an enhanced learning algorithm, the generation of interference strategies in a complex electromagnetic environment can be completed within 0.1 seconds. -The US military ALQ-214V4 has achieved an autonomous recognition rate of 80% of unknown signals. 2. **Intelligent tactical decision-making assistance** -Construct a digital twin battlefield system to simulate a tactical combination of the order of 10^6 through enhanced learning of multiple agents. -NATO MSaaS platform verification shows a 300% increase in decision-making speed. 3. **Autonomous collaborative combat network** -Apply federal learning technology to realize distributed intelligence of ships, drones, and satellites： -Delay：<50ms (inter-star laser communication) -Data throughput: ≥1Tbps (Terahertz frequency band) ###3. Key technological breakthrough points 1. **Quantum machine learning chip** -Solve the bottleneck problem of traditional computing architecture in complex electromagnetic computing -IBM quantum processor has achieved 128 qubits military application verification 2. **Defense against samples** -Develop an anti-spoofing algorithm for AI stealth system： -Use generative adversarial network (GAN) to enhance system robustness -Test index: Anti-interference ability increased by 40% 3. **Intelligent energy management** -Nuclear energy-power distribution system based on in-depth intensive learning： -The AI energy system of Japan's Canglong-class submarine saves 12% of energy consumption ###4. Implementation path recommendations 1. Recently (<3 years)： -Complete the radiation reinforcement of special chips for ship-based AI (such as Cambrian MLU590) -Establish a database of stealth characteristics (≥1EB level) 2. Medium term (3-5 years)： -Realize intelligent command of manned-unmanned hybrid formations -Deploy carrier-based edge computing nodes (computing power ≥16PFLOPS) 3. Long-term (5-8 years)： -Real-time update of the ship's digital twin -Cross-domain intelligent collaboration guaranteed by quantum communication It should be noted that all technological developments must strictly comply with the restrictions on camouflage in the international maritime safety conventions (such as SOLAS) and the Law of Armed conflict. It is recommended to conduct more than 10^7 simulation verification through a virtual shooting range (such as the US military ONE-SAF) before actual installation.