Structural mechanics is commonly modeled by (systems of) partial differential equations (PDEs). Except for very simple cases where analytical solutions exist, the use of numerical methods required to find approximate solutions. However, many problems practical interest, computational cost classical solvers running on classical, that is, silicon-based computer hardware, becomes prohibitive. Quantum computing, though still in its infancy, holds promise enabling a new generation algorithms can...

This paper explores the potential application of quantum and hybrid quantum–classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus 33-bus test systems. A systematic performance comparison is also among quantum, quantum–classical, classical networks. The (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) error, (v) process stability. results show that developed network outperforms both networks, hence...

The wide adoption of composite structures in the aerospace industry requires reliable numerical methods to account for effects various damage mechanisms, including delamination. Cohesive elements are a versatile and physically representative way modelling However, using their standard form which conforms solid substrate elements, multiple required narrow cohesive zone, thereby requiring an excessively fine mesh hindering applicability practical scenarios. present work focuses on...

Modeling open hole failure of composites is a complex task, consisting in highly nonlinear response with interacting modes. Numerical modeling this phenomenon has traditionally been based on the finite element method, but requires to tradeoff between high fidelity and computational cost. To mitigate shortcoming, recent work leveraged machine learning predict strength composite specimens. Here, we also propose using data-based models tackle from classification point view. More specifically,...

The wide adoption of composite structures in the aerospace industry requires reliable numerical methods to account for effects various damage mechanisms, including delamination. Cohesive elements are a versatile and physically representative way modelling However, using their standard form which conforms solid substrate elements, multiple required narrow cohesive zone, thereby requiring an excessively fine mesh hindering applicability practical scenarios. present work focuses on...

Different hybrid quantum-classical algorithms have recently been developed as a near-term way to solve linear systems of equations on quantum devices. However, the focus has so far mostly methods, rather than problems that they need tackle. In fact, these run real hardware only for in physics, such Hamiltonians few qubits systems. These are particularly favorable hardware, since their matrices sum just unitary terms and shallow circuits required estimate cost function. many interesting...

This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on 4-bus 33-bus test systems. A systematic performance comparison is also among quantum, quantum-classical, classical networks. The (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) error, (v) process stability. results show that developed network outperforms both networks, hence...