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Natural hazards, including hurricanes and earthquakes, can escalate into catastrophic societal events due to the destruction of the built environment. To minimize the impact of such hazards on vulnerable communities, civil infrastructure must be designed with performance criteria that prioritize public safety and ensure continuous operation. The National Science Foundation funded Natural Hazards Engineering Research Infrastructure (NHERI) program focuses on advancing the development of resilient infrastructure. The NHERI Lehigh Real-time Multi-directional Simulation Experimental Facility (EF) is one of the facilities within this program. The facility serves as an open-access research hub, offering advanced technologies and engineering tools to develop innovative solutions for natural hazard mitigation. It is uniquely equipped to perform large-scale, multi-directional structural testing in real-time using a cyber-physical simulation technique known as real-time hybrid simulation. This technique enables researchers to model entire systems subjected to dynamic loads at a full scale, allowing for realistic assessments of infrastructure responses to specific hazard scenarios and the development of effective mitigation strategies. This paper explores how cyber-physical simulation has revolutionized research in natural hazards engineering and its influence on engineering practices. It highlights several ongoing projects at the NHERI Lehigh EF aimed at enhancing community resilience in hazard-prone regions. The paper also discusses the planned expansion of the EF, which aims to broaden its focus to include a wider range of natural hazards, and infrastructure systems. This expansion will incorporate both physical and computational resources to enhance the understanding of fluid interactions in combined natural hazards and climate change impacts on coastal and offshore infrastructure. The NHERI Lehigh EF represents a transformative facility that is reshaping natural hazards research and will continue to play a pivotal role in the development of risk management strategies for more resilient communities. 

Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But whatisthis new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants—with a range of prior experience using the assistant—as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants arebimodal: inacceleration mode, the programmer knows what to do next and uses Copilot to get there faster; inexploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants. 

Abstract We formulate a class of stochastic partial differential equations based on Kelvin’s circulation theorem for ideal fluids. In these models, the velocity field is randomly transported by white-noise vector fields, as well as by its own average over realizations of this noise. We call these systems the Lagrangian averaged stochastic advection by Lie transport (LA SALT) equations. These equations are nonlinear and non-local, in both physical and probability space. Before taking this average, the equations recover the Stochastic Advection by Lie Transport (SALT) fluid equations introduced by Holm (Proc R Soc A 471(2176):20140963, 2015). Remarkably, the introduction of the non-locality in probability space in the form of momentum transported by its own mean velocity gives rise to a closed equation for the expectation field which comprises Navier–Stokes equations with Lie–Laplacian ‘dissipation’. As such, this form of non-locality provides a regularization mechanism. The formalism we develop is closely connected to the stochastic Weber velocity framework of Constantin and Iyer (Commun Pure Appl Math 61(3):330–345, 2008) in the case when the noise correlates are taken to be the constant basis vectors in $$\mathbb {R}^3$$ R 3 and, thus, the Lie–Laplacian reduces to the usual Laplacian. We extend this class of equations to allow for advected quantities to be present and affect the flow through exchange of kinetic and potential energies. The statistics of the solutions for the LA SALT fluid equations are found to be changing dynamically due to an array of intricate correlations among the physical variables. The statistical properties of the LA SALT physical variables propagate as local evolutionary equations which when spatially integrated become dynamical equations for the variances of the fluctuations. Essentially, the LA SALT theory is a non-equilibrium stochastic linear response theory for fluctuations in SALT fluids with advected quantities.