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Simulating KH-, RT-, or RM-driven mixing using direct numerical simulations (DNS) can be prohibitively expensive because all the spatial and temporal scales have to be resolved, making approaches such as Reynolds-averaged Navier–Stokes (RANS) often the more favorable engineering option for applications like ICF. To this day, no DNS has been performed for ICF even on the largest supercomputers, as the resolution requirements are too stringent.⁸ However, RANS approaches also face their own challenges: RANS is based on the Reynolds decomposition of a flow where mean quantities are intended to represent an average over an ensemble of realizations, which is often replaced by a spatial average due to the scarcity of ensemble datasets. Replacing ensemble averages by space averages may be appropriate for flows that are in homogenous-, isotropic-, and fully developed turbulent states in which spatial, temporal, and ensemble averaging are often equivalent. However, most HED hydrodynamic experiments involve transitional periods in which the flow is neither homogeneous nor isotropic nor fully developed but may contain large-scale unsteady dynamics; thus, the equivalency of averaging can no longer be assumed. Yet, RANS models often still require to be initialized in such states of turbulence, and knowing how and when to initialize them in a transitional state is, therefore, challenging and is still poorly understood.

The goal of this paper is to develop a strategy allowing the initialization of a RANS model to describe an unsteady transitional RM-induced flow. We seek to examine how ensemble-averaged quantities evolve during the transition to turbulence based on some of the first ensemble experiments repeated under HED conditions. Our strategy involves using 3D high-resolution implicit large eddy simulations (ILES) to supplement the experiments and both initialize and validate the RANS model. We use the Besnard–Harlow–Rauenzahn (BHR) model,^9–12 specifically designed to predict variable-density turbulent physics involved in flows like RM. Previous studies have considered different ways of initializing the BHR model.

Reliably guiding and capturing optical waves is central to the functioning of various contemporary technologies, including communication and information processing systems. The most conventional approach to guide light waves leverages the total internal reflection of optical fibers and other similar structures, yet recently physicists have been exploring the potential of techniques based on other physical mechanisms.

Researchers at University of Southern California recently devised a highly innovative approach for trapping light. This method, introduced in Nature Physics, exploits the exotic properties of Lagrange points, the same equilibrium points that govern the orbits of primordial celestial bodies, such as so-called Trojan asteroids in the sun-Jupiter system.

“The discovery of Lagrange points, which happens to be pivotal in this research, can be traced back to the early work of Leonhard Euler and Joseph-Louis Lagrange, which found that at these locations, the gravitational attraction exerted by two large bodies can be precisely counterbalanced by centrifugal forces,” Mercedeh Khajavikhan and Demetrios N. Christodoulides, co-authors of the paper, told Phys.org.

SpaceX is teaming up with Northrop Grumman today to deliver more than 8,000 pounds of cargo, fresh food and scientific experiments to astronauts on the International Space Station.

The NG-20 resupply mission will take off from the Space Force’s Cape Canaveral in Florida on a SpaceX Falcon 9 rocket at around 12:07 p.m. EST. Northrop’s Cygnus cargo capsule will arrive at the International Space Station on February 1.

Northrop has been launching Cygnus to the ISS for resupply missions using its own Antares rocket since 2013, with the exception of just two missions that used a United Launch Alliance Atlas 5. But Northrop retired that version of Antares last year, and the next version — an all-American launch vehicle called Antares 330, which it is developing with Firefly Aerospace — will not be ready to fly until around mid-2025.

Japan’s moon lander has come back to life, the space agency said Monday, enabling the craft to proceed with its mission of investigating the lunar surface despite its rocky start.

The surprise announcement was a boost to Japan’s space program, nine days after the Smart Lander for Investigating Moon (SLIM) touched down at a wonky angle that left its solar panels facing the wrong way.

“Last evening we succeeded in establishing communication with SLIM, and resumed operations!” JAXA said on social media platform X, posting a grainy image of a lunar rock known as a “toy poodle”

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A bizarre object discovered by the James Webb Space Telescope may be a pair of ‘rogue’ planets ― but a new study finds they are emitting radio signals rarely seen from other worlds.

Although a significant number of neuromorphic devices applied to RC have been reported in recent years, the majority of these efforts have focused on shallow-RC with monotonic reservoir state spaces¹⁹. This can be attributed to the heavy reliance on monotonic carrier dynamics when using reported neuromorphic devices as reservoirs to map sequence signals, which gives rise to several noteworthy issues for RC when performing different spatiotemporal tasks. One major issue is that the narrow range ratio of spatial characteristics makes it difficult to extract the diversity spatial feature of sequence signal, which greatly limits the richness of the reservoir space state. As a result, during the process of mapping complex sequence signals, the reservoir state tends to overlap, making it difficult to effectively separate the spatial characteristics within complex information and subsequently reducing recognition accuracy. Another issue is the limited rang ratio of temporal characteristic, which hinders efficient extraction of temporal feature from sequential signals with diverse time-scales. For example, when performing dynamic trajectory prediction with abundant time-scales, the limited range ratio of temporal characteristic is difficult to adapt to the signal with different temporal feature, which severely limit the correlation of prediction. Despite researchers have achieved multi-scale temporal characteristics by increasing the number of signal modes in the input layer based on shallow-RC networks²⁰, as shown in the Supplement Information Fig. S1, the limitation of shallow-RC on spatial characteristics remain unresolved. Furthermore, increasing the input layer also means the requirement of more encoding design for sequence signals and the utilization of more physical devices to receive different modes of physical signals. This significantly increases the signal error rate and pre-processing cost of the input signals, which is detrimental to the robustness of RC. Therefore, developing new neuromorphic reservoir devices along with new RC networks to simultaneously meet large-scale spatial and temporal characteristics are highly required, which is crucial for achieving high-performance recognition and prediction in complex spatiotemporal tasks for RC networks.

Interestingly, primates in nature are able to quickly and accurately recognize complex object information, such as facial recognition, with the help of advanced synaptic dynamics mechanisms. Brain science research on primates has confirmed^20,21,22 that primates use a distributed memory characteristic for processing complex information. When the nervous system processes a task, each neuron and neural circuit processes only a part of the information and generates a part of the output. For example, as shown in Fig. 1a, when a primate observes an unfamiliar face, neurons in the temporal polar (TP) region (blue) respond to familiar eye features, forming TP feature memory. Neuron cells in the anterior-medial (AM) region respond to unfamiliar lip features, forming AM feature memory²³. In this way, all outputs are integrated by the cerebral cortex to form the final output result, significantly improving the computational efficiency and accuracy for complex information processing. The physiological significance of distributed memory characteristics in primates serves as inspiration for the design of physical node devices with distributed reservoir states in the reservoir layer of the RC system. These devices are intended to facilitate the distributed mapping of spatiotemporal signals. However, to date, no such devices have been demonstrated.

In this work, inspired by the distributed memory characteristic of primates, an ultra-short channel organic neuromorphic vertical field effect transistor with distributed reservoir states is proposed and used to implement grouped-RC networks. By coupling multivariate physical mechanisms into a single device, the dynamic states of carriers are greatly enriched. As reservoir nodes, sequential signals can be mapped to a distributed reservoir state space by various carrier dynamics, rather than by monotonic carrier dynamics. Additionally, a vertical architecture with ultra-short nanometers transport distance is adopted to eliminate the driving force of the dissociation exciton, thereby improving the feedback strength of the device and the reducing the overlap between different reservoir state space, which only cause negligible additional power. Consequently, the device serves as a reservoir capable of mapping sequential signals into distributed reservoir state space with 1,152 reservoir states, and the range ratio of temporal (key parameters for prediction) and spatial characteristics (key parameters for recognition) can simultaneously reach 2,640 and 650, respectively, which are superior to the reported neuromorphic devices. Therefore, the grouped-RC network implemented based on the device can simultaneously meet the requirements of two different spatiotemporal types task (broad-spectrum image recognition and dynamic trajectory prediction) and exhibits over 94% recognition accuracy and over 95% prediction correlation, respectively. This work proposes a strategy for developing neural hardware for complex reservoir computing networks and has great potential in the development of a new generation of artificial neuromorphic hardware and brain-like computing.

Columbia researchers analyzing images from NASA’s James Webb Space Telescope have found that galaxies in the early universe are often flat and elongated, like breadsticks—and are rarely round, like balls of pizza dough.

“Roughly 50 to 80% of the galaxies we studied appear to be flattened in two dimensions,” explained Viraj Pandya, a NASA Hubble Fellow at Columbia University and the lead author of a new paper slated to appear in The Astrophysical Journal that outlines the findings. The paper is currently published on the arXiv preprint server.

“Galaxies that look like long, thin breadsticks seem to be very common in the early universe, which is surprising since they are uncommon among galaxies in the present-day universe.”

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