The aviation industry faces various challenges in meeting long-term sustainability goals amidst surging demand for air travel and growing environmental concerns of the general public. The year 2050 is set as an ambitious goal for net zero emissions, a substantial reduction in carbon dioxide emissions per passenger kilometer flown, major improvements in aircraft energy efficiency, and a development towards autonomous, intelligent operations. This review explores the pivotal role of advancements in engineering for achieving sustainability in aviation. Through a comprehensive review of existing literature and case studies, our work highlights how innovations in all aspects of aircraft engineering coupled with operations-related technologies, offer promising solutions to mitigate environmental impact, enhance efficiency, and ensure long-term sustainability in aviation operations. To discuss the necessary advances, we promote the so-called ‘DESIGN and be SMART’ framework, consisting of eleven complementary engineering challenges towards reaching sustainability. To address the high safety levels reached in air transportation, our DESIGN and be SMART framework also addresses the safety assurance challenge that is overarching each of the eleven engineering challenges. We believe that through an orchestrated integration of hardware advancements with innovative software solutions, and novel safety assurance methods, the aviation industry can realize synergistic benefits that drive sustainable growth of air transportation. Our review contributes to such an orchestration by describing the status quo and research challenges ahead.@en

Hamiltonian systems represent an important class of dynamical systems such as pendulums, molecular dynamics, and cosmic systems. The choice of solvers is significant to the accuracy when simulating Hamiltonian systems, where symplectic solvers show great significance. Recent advances in neural network-based hypersolvers, though achieve competitive results, still lack the symplecity necessary for reliable simulations, especially over long time horizons. To alleviate this, we introduce Hyperverlet, a new hypersolver composing the traditional, symplectic velocity Verlet and symplectic neural network-based solvers. More specifically, we propose a parameterization of symplectic neural networks and prove that hyperbolic tangent is r-finite expanding the set of allowable activation functions for symplectic neural networks, improving the accuracy. Extensive experiments on a spring-mass and a pendulum system justify the design choices and suggest that Hyperverlet outperforms both traditional solvers and hypersolvers.@en