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Top 9 Cutting-Edge MATLAB AI & Digital Tech Projects for 2025–2026

MATLABSolutions. Nov 26 2025 · 7 min read
Top 9 MATLAB AI Projects 2025–2026 with Full Code

  1. MATLAB Copilot + Gemini – Generate Full Simulink Models with One Sentence.  
    response = matlab.copilot.ask("Build a complete Simulink model of a quadcopter with PID altitude control"); 
    openModel(response.modelName);
  2. YOLOv11 on NVIDIA Jetson Orin – 45 FPS Real-Time Detection.
    detector = yolov11ObjectDetector("yolov11n",classes);
cfg = coder.config('lib');
cfg.Hardware = coder.hardware('NVIDIA Jetson');
cfg.DeepLearningConfig = coder.DeepLearningConfig('tensorrt');
codegen -config cfg yolov11Detect -args {ones(640,640,3,'uint8')}
  3. Battery Digital Twin with State-of-Health Prediction.  
    layers = [sequenceInputLayer(8); lstmLayer(256,'OutputMode','sequence'); gruLayer(128); fullyConnectedLayer(1); regressionLayer];
net = trainNetwork(XTrain,YTrain,layers,options);
sim('BatteryDigitalTwin_2025b.slx')
  4. Agentic AI That Designs Heat Exchangers Automatically (MCP Server). 
    mcpServer = mcp.CoreServer(); mcpServer.start();
mcpServer.addTool(mcp.Tool(@runHeatExchangerSimulink,"name","HX_simulator"));
agent = mcp.Agent("claude-3-5-sonnet-20241022",mcpServer);
agent.run("Design a plate heat exchanger for 500 kW duty, max ΔT 3°C");
  5. 6G AI-Native Beamforming with New SADEA Optimizer. 
    array = phased.URA([16 16],0.5);
optimizer = comm.SADEAOptimizer('Element',array);
w = optimizer.optimize(@(w)-spectralEfficiency(w,array,users));
pattern(array,30e9,-180:180,-90:90,'Weights',w);
  6. Diffusion Model – Generate Unlimited Synthetic Sensor Data 
    diffModel = diffusionModel('unet1d',SequenceLength=1000,NumChannels=12);
train(diffModel,sequences);
newSignals = generate(diffModel,500);
  7. LLM-Powered Fault Diagnosis Chatbot Inside MATLAB. 
    chat = matlab.llm.Chat("gpt-4o-mini");
chat.addTool(@readPLCdata);
chat.addTool(@runFaultClassifier);
response = chat.ask("Bearing temp 12°C high, vibration RMS +40%. What’s wrong?");
disp(response);
  8. Reinforcement Learning – Autonomous Drone Racing (Sim-to-Real) 
    env = rl.createEnv(@droneRacingEnv);
agent = rlDDPGAgent(actor,critic);
train(agent,env,'MaxEpisodes',3000);
  9. Multi-Agent Factory Layout Optimization with OpenAI Swarm. 
    swarm = openai.Swarm("gpt-4o");
agents = swarm.createAgents(10,"factory_layout_expert");
result = swarm.run(agents,"Optimize this factory for max throughput and lowest energy");