Optimality-informed neural networks for lunar landing optimization

Autonomous lunar spacecraft landing represents one of the most complex challenges in nonlinear optimal control. Optimality-informed neural networks (OINN) offer a novel solution by directly integrating Pontryagin's and Hamilton-Jacobi-Bellman conditions into the network architecture, eliminating the need for precomputed trajectories and ensuring real-time performance with fixed computational cost. This approach, applicable to multiple industrial domains, demonstrates how artificial intelligence can solve optimization problems with dynamic constraints without relying on large volumes of data. At Q2BSTUDIO we develop custom applications and custom software that integrate these principles, as well as AWS and Azure cloud services to scale training, cybersecurity to protect models, and business intelligence services with Power BI to visualize results. Our AI agents enable real-time decision automation, transforming theory into robust and efficient business solutions.