The discovery of ultra-strong materials such as carbon nanotubes and diamond nano-thread structures has recently motivated an enhanced interest for the physics of Space Elevators connecting the Earth with outer space. A new concept has recently emerged in space elevator physics: Rotating Space Elevators (RSE) [Golubovic, L. & Knudsen, S. (2009). Classical and statistical mechanics of celestial scale spinning strings: Rotating space elevators. Europhysics Letters 86(3), 34001.]. Objects sliding along rotating RSE string (sliding climbers) do not require internal engines or propulsion to be transported from the Earth's surface into outer space. Here we address the physics of a special RSE family, Uniform Stress Rotating Space Elevators (USRSE), characterized by constant tensile stress along the string. From the point of view of materials science, this condition provides the best control of string’s global integrity. We introduce an advanced analytic approach to obtain the dynamic equilibrium configurations of USRSE strings. We use our results to discuss the applications of USRSE for spacecraft launching.

Abstract. As a novel optical platform, random fiber laser (RFL) exhibits multiple metastable energy landscapes due to its disordered feedback mechanism and nonlinear effects, which make it an ideal platform for the study of nonequilibrium statistical physics and multiple nonlinear optical effects. We systematically explores the progress of cutting-edge research on RFL from three key perspectives: complex physical properties, time-domain dynamics, and frequency-domain dynamics in conjunction with theoretical models. In particular, the refinement of the modeling of Rayleigh scattering promotes the study of the replica symmetry breaking phenomenon of RFL. On the basis of this theoretical study, the article elucidates the innovative applications of RFL in several fields, especially providing new possibilities in the field of laser-driven inertial confinement fusion. Finally, the article explores the challenges and possibilities of RFL integration with cutting-edge fields, such as neural networks, and provides an outlook on its prospects for multidisciplinary fusion applications.

Abstract Learning is a complex dynamical process shaped by a range of interconnected decisions. Careful design of hyperparameter schedules for artificial neural networks or efficient allocation of cognitive resources by biological learners can dramatically affect performance. Yet, theoretical understanding of optimal learning strategies remains sparse, especially due to the intricate interplay between evolving metaparameters and nonlinear learning dynamics. The search for optimal protocols is further hindered by the high dimensionality of the learning space, often resulting in predominantly heuristic, difficult to interpret, and computationally demanding solutions. Here, we combine statistical physics with control theory in a unified theoretical framework to identify optimal learning protocols in prototypical neural network models. In the high-dimensional limit, we derive closed-form ordinary differential equations that track online stochastic gradient descent through low-dimensional order parameters. We formulate the design of learning protocols as an optimal control problem directly on the dynamics of the order parameters with the goal of minimizing the generalization error. This formulation encompasses a variety of learning scenarios, optimization constraints, and control budgets. We apply it to representative cases, including optimal curricula, adaptive dropout regularization and noise schedules in denoising autoencoders. We find nontrivial yet interpretable strategies highlighting how optimal protocols mediate learning trade-offs. Our results establish a principled foundation for understanding and designing optimal protocols and suggest a path toward a theory of meta-learning grounded in statistical physics.