Predicting Projectile Motion Using Artificial Intelligence & Machine Learning - With App and Visualization

In this research project, I explored the integration of classical physics equations with a basic AI-assisted web application to predict projectile motion outcomes. My aim was to bridge theoretical physics and modern computational tools by simulating projectile motion using user inputs like velocity, angle, gravitational constant, and axis of projection. To achieve this, I built a web-based simulator using Lovable.dev, which enables users to input real-time data and receive accurate outputs such as range, maximum height, time of flight, and even the trajectory equations. Although the calculations behind the scenes were based on classical equations of motion, the app was structured in such a way that it mimics the early stages of an intelligent assistant by generating human-readable explanations and responses. A dataset of 15+ projectile cases was used, and each data point helped validate the theoretical outputs with the app’s real-time calculations. The results were remarkably consistent with expected theoretical values, confirming that even with some machine learning training, the app serves as a reliable tool to understand projectile motion visually and interactively. One of the main findings is the potential of combining physics and AI-powered systems to create tools that are both educational & functional. The project proves that machine learning—or at least AI structuring—can be applied meaningfully even in classical physics domains. It also highlights how developing interactive platforms can enhance both understanding and user engagement, especially for students and learners. This work lays the foundation for more complex implementations, where actual ML models could be trained on projectile data and adapt to different parameters, environments, and even real-world sensor inputs. The integration of educational content with AI simulation also demonstrates the potential of AI to revolutionize the way we learn and interact with core scientific principles.

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