In this thesis, a new data-driven finite element is developed, which is referred to as a neuromorphic element (designated as NmT2). Its goal is to reduce the computational expense of FEA models with- out compromising solution accuracy by embedding a neural network, trained on an element level. The neural network is developed such that the traditional trial-and-error approach to determin- ing its hyperparameters may be bypassed. This is achieved through a multi-objective optimization algorithm that builds networks with random configurations and uses Latin Hypercube sampling to test them on a fraction of the overall data repository. Once the algorithm reaches a state of diminish- ing returns over the development of multiple networks, the program is halted and the best perform- ing neural network is saved. The resultant network is then trained over the entire data repository consisting of over half a million datasets. The entire process of a self-designing neural network is called a neuromorphic engine. The neuromorphic engine is designed to determine the local nodal force vector of a truss mem- ber based on the structure’s geometry and axial nodal displacements. Axial tension and compres- sion are the two modes of loading that are considered and are pushed to the nonlinear regimes by including post-buckling and material plasticity. In addition, the user is provided with the option of including structural defects in the truss members. Once trained, the neuromorphic engine can be inserted within a user-element subroutine and deployed in ABAQUS. The neuromorphic element is essentially a truss element which includes the deformation ca- pabilities of beam elements. Unlike traditional FEA methods requiring multiple beam elements, a single NmT2 element can be used when meshing a truss member to model complex behaviour such as post-buckling deformation. To test the capabilities of the neuromorphic element, three case studies are designed as a proof of concept, comparing the performance of NmT2 elements against traditional FEA elements (T2D2 or B22). Overall, the NmT2 elements managed to accel- erate the computing time of an FEA model by up to 1,000%, while maintaining solution accuracy within 5%. These results affirm the potential of neural networks within active FEA simulations in the field of data-driven computational mechanics as a means to define complex nonlinear element formulations.

Lunar exploration initially started as a race between two superpowers to see who had the highest technical capabilities to send a human onto the Moon’s surface. Until recently, the frequency of Moon missions has been in decline, but in the last couple years there has been an increasing interest in returning. Scientific studies and observations will be performed and will give humanity a better understanding of the solar system and our place in it. As a result there will be a need for a direct communication line between the lunar surface and Earth, such that rovers can be controlled in real-time and immediately relay their data back, independent of their location on the Moon.