Applications of AI in Aerospace with MathWorks

Aerospace development today operates within a complex, high-stakes landscape shaped by the pursuit of advancing aviation, space exploration, and defence technologies. Companies in this sector face multifaceted challenges, starting with the astronomical costs and risks associated with R&D, manufacturing, and testing. Developing cutting-edge aircraft, spacecraft, or propulsion systems demands billions of rupees in investment over decades, but returns are often not assured. Technical complexity is another hurdle: aerospace systems must operate flawlessly under extreme conditions—such as vacuum, radiation, or supersonic speeds—requiring breakthroughs in materials science, thermal management, and propulsion. Regulatory compliance adds layers of difficulty, as projects must meet stringent safety and environmental standards across global jurisdictions, which can delay timelines and inflate budgets. Sustainability pressures are mounting, with governments and consumers demanding greener technologies, pushing firms to innovate in electric propulsion, alternative fuels, and lightweight composites while balancing performance and cost. Supply chain vulnerabilities also loom large, as aerospace relies on specialized components from global suppliers, exposing projects to geopolitical tensions, trade restrictions, or disruptions. The aerospace industry is undergoing a paradigm shift today with the integration of AI, particularly through platforms like MATLAB and Simulink (more on this later). AI is revolutionizing aircraft design by enabling generative design algorithms that explore thousands of configurations to optimize aerodynamics, weight, and fuel efficiency. For example, a major commercial airliner manufacturer in Europe uses AI to reduce aircraft weight while maintaining structural integrity.

The Rise of Digital Twins and AI Co-Pilots in Aerospace
Digital twins in the aerospace sector are virtual replicas of physical aircraft or systems that use real-time or historical data along with machine learning algorithms to analyze and predict system behaviour. These virtual models offer various benefits, including improved forecasting, advanced diagnostics, optimized fleet management, comprehensive lifecycle management, and enhanced operational performance.

Applications of Digital Twins in Aerospace:

• Aircraft Design: Digital twins facilitate virtual prototyping, optimized aerodynamics, and improved structural enhancements, which leads to more precise maintenance methods. Virtual prototyping allows engineers to test designs efficiently using advanced simulations, which assess aspects like take-off, landing, and system responses to different scenarios, accelerating the design process and minimizing costly modifications. Aero plane manufacturers use digital twins to predict the performance of aircraft components over their lifecycle, resulting in a substantial improvement in the quality of parts.
• Performance and Operations: Digital twins enhance aircraft performance and operations, empower pilots, and facilitate remote monitoring for improved operational efficiency. They provide realistic flight simulators for pilot training, enabling pilots to practice various scenarios and emergency procedures. Real-time data visualization helps pilots make informed decisions during flights, considering factors such as aircraft conditions and weather patterns.
• Maintenance: Digital twins are valuable in maintenance practices, supporting scheduled, unscheduled, preventive, and predictive maintenance activities. They help maintain the structural integrity of aircraft by allowing engineers to analyze stress and fatigue through simulating materials and components, which results in stronger structures, enhanced fuel efficiency, and reduced operational costs. Sensors placed on landing gear failure points can provide real-time data to predict malfunctions or diagnose the remaining lifecycle of the landing gear.

AI Co-Pilots:
AI co-pilots in aviation represent a significant advancement in the integration of AI into flight operations. It is essentially a virtual assistant that operates alongside human pilots, utilizing advanced machine learning and data analysis techniques. It continuously monitors a wide array of flight data, including engine performance, weather conditions, and air traffic updates. The AI co-pilot's primary role is to assist in cognitive tasks without replacing the pilot's judgment or authority. These systems are designed to enhance safety, efficiency, and decision-making for human pilots by providing real-time assistance and monitoring capabilities. Key features of AI co-pilots include real time monitoring, attention tracking, adaptive decision making and task automation.

Let us now look at how MathWorks helps in this space.

Brief Introduction to MathWork

MathWorks is a leading developer of mathematical computing software that specializes in two primary software products: MATLAB and Simulink. MATLAB is a programming environment designed for engineers and scientists, enabling algorithm development, data analysis, visualization, and numeric computation. Simulink is a block diagram environment for simulation and Model-Based Design of multi-domain and embedded engineering systems. These tools are used across various industries including automotive, aerospace, communications, electronics, and industrial automation.

MATLAB offers specialized toolboxes that empower aerospace engineers to leverage AI across the development lifecycle:  

• Control System Toolbox: This toolbox is pivotal for designing autonomous flight systems. Engineers use it to model flight dynamics, design PID (Proportional – Integral – Derivative) controllers, and simulate fault scenarios. For example, AI-enhanced autopilot systems rely on this toolbox to process real-time sensor data and adjust control surfaces autonomously. 
• Communications Toolbox:  Essential for optimizing wireless systems, this toolbox supports AI-driven signal processing, error correction, and network design. It enables simulations of satellite communication links, ensuring robustness against interference—a key requirement for UAVs and military aircraft. 
• Predictive Maintenance Toolbox: Integrated with MATLAB’s machine learning libraries, this toolbox analyzes vibration, temperature, and pressure data to forecast component failures. Airlines use it to schedule maintenance, reducing operational disruptions. 

Engineering companies can leverage MATLAB and Simulink ecosystems to integrate AI into aerospace manufacturing workflows, addressing challenges like design complexity, quality control, and system validation. By combining model-based design with AI capabilities, these tools enable manufacturers to optimize multi-domain systems, automate defect detection, and accelerate certification processes while maintaining compliance with stringent aerospace standards. For complex system design, MathWorks tools enable AI-powered trade studies and multi-objective optimization. Engineers can create reduced-order models from simulation data, run automated design-of-experiments, and use MATLAB’s AutoML features to identify optimal configurations for propulsion systems or aerodynamic surfaces.

Autonomous system development benefits from MATLAB / Simulink’s unified environment for AI algorithm development and hardware deployment. Manufacturers designing UAVs or satellite constellations use reinforcement learning in MATLAB to train obstacle-avoidance algorithms against synthetic 3D environments modelled in Simulink. MathWorks tools also optimize manufacturing processes through AI-driven digital twins. Aerospace companies create physics-based models of production lines in Simulink, and then apply MATLAB’s neural networks to predict bottlenecks in composite layup processes or laser welding operations. The Simulink PLC Coder further bridges design and production by automatically generating IEC 61131-3 code (international standard for programmable controller programming languages required for industrial automation) for industrial machinery from AI-optimized models.

For supply chain resilience, MATLAB’s forecasting tools analyze supplier risk factors and material lead times using machine learning models trained on historical disruption data. During the 2024 semiconductor shortage, major aerospace contractors employed these models to dynamically reroute titanium supply chains, minimizing production delays as compared to competitors using traditional ERP systems. Simulink’s System Composer simultaneously allowed cross-functional teams to model alternative logistics networks, assessing AI-proposed solutions against cost, sustainability, and reliability metrics.

In summary, MATLAB’s AI ecosystem bridges the gap between theoretical models and practical aerospace applications, fostering innovation in design, communication, and operational efficiency. MATLAB and Simulink enable aerospace companies to seamlessly integrate AI into production by providing advanced tools for predictive maintenance, autonomous system design, digital twin creation, and optimized communication systems, streamlining innovation.