Workflow di IA end-to-end

Define requirements, prepare data, train deep learning networks, verify robustness, integrate networks into Simulink, and deploy models. (Da R2024b)

• PASSAGGIO 1: Define Requirements for Battery State of Charge Estimation
• PASSAGGIO 2: Prepare Data for Battery State of Charge Estimation Using Deep Learning
• PASSAGGIO 3: Train Deep Learning Network for Battery State of Charge Estimation
• PASSAGGIO 4: Compress Deep Learning Network for Battery State of Charge Estimation
• PASSAGGIO 5: Test and Verify Deep Learning Network for Battery State of Charge Estimation
• PASSAGGIO 6: Integrate AI Model into Simulink for Battery State of Charge Estimation
• PASSAGGIO 7: Generate Code for Battery State of Charge Estimation Using Deep Learning
• PASSAGGIO 8: Deploy Code for Battery State of Charge Estimation Using Deep Learning

Train and compress a sequence classification network using pruning, projection, and quantization to meet a fixed memory requirement. (Da R2025a)

• PASSAGGIO 1: Train Sequence Classification Network for Road Damage Detection
• PASSAGGIO 2: Compress Sequence Classification Network for Road Damage Detection
• PASSAGGIO 3: Tune Compression Parameters for Sequence Classification Network for Road Damage Detection
• PASSAGGIO 4: Generate Simulink Model from Sequence Classification Network for Road Damage Detection

This example shows how to develop and verify a deep learning model that classifies electrocardiogram (ECG) signals to detect atrial fibrillation (AFib). (Da R2026a)

• PASSAGGIO 1: Define Requirements for ECG Signal Classification Using Deep Learning
• PASSAGGIO 2: Prepare Data for ECG Signal Classification
• PASSAGGIO 3: Train Deep Learning Network for ECG Signal Classification
• PASSAGGIO 4: Improve Adversarial Robustness of Deep Learning Network for ECG Signal Classification
• PASSAGGIO 5: Test Deep Learning Network for ECG Signal Classification
• PASSAGGIO 6: Out-of-Distribution Detection for ECG Signal Classification
• PASSAGGIO 7: Uncertainty Quantification for ECG Signal Classification
• PASSAGGIO 8: Investigate ECG Signal Classifications Using Grad-CAM
• PASSAGGIO 9: Build Deep Learning ECG Signal Classification App Using App Designer

Verify a set of neural networks trained for airborne collision avoidance integrated into a Simulink model using formal methods and scenario-based closed-loop testing. (Da R2026a)

• PASSAGGIO 1: Explore ACAS Xu Neural Networks
• PASSAGGIO 2: Verify Local Robustness of ACAS Xu Neural Networks
• PASSAGGIO 3: Verify Global Stability of ACAS Xu Neural Networks
• PASSAGGIO 4: Verify Global Stability of ACAS Xu Neural Network Using Adaptive Mesh
• PASSAGGIO 5: Verify VNN-COMP Benchmark for ACAS Xu Neural Networks
• PASSAGGIO 6: Verify VNN-COMP Benchmark for ACAS Xu Neural Networks Using α,β-CROWN
• PASSAGGIO 7: Define and Verify AI Constituent Requirements for ACAS Xu Neural Networks
• PASSAGGIO 8: Integrate ACAS Xu Neural Networks into Simulink
• PASSAGGIO 9: Define and Verify AI System Requirements for ACAS Xu Neural Networks Integrated Into Simulink

This example shows how to verify a deep learning system for airborne applications and is based on the work in [5,6,7], which includes the development and verification activities required by DO-178C [1], ARP4754A [2], and prospective EASA and FAA guidelines [3,4]. (Da R2023b)