Physics-Informed Neural Networks for Designing Spacecraft Trajectories

This thesis proposes an unsupervised Physics-Informed Neural Network (PINN) for solving optimal control problems with the direct method to design and optimize transfer trajectories. The network adheres analytically to boundary conditions and includes the objective fitness as regularization in its loss function. A test scenario of a planar Earth...

Topology Optimization and Physics-Informed Neural Networks for Metamaterial Optics Design

The development of optical metamaterials in recent years has enabled the design of novel optical devices with exciting properties and applications ranging across many fields, including in scientific instrumentation for space missions. This in<br/>turn has led to demand for computational methods which can produce efficient device designs....