This paper describes the open-source code Enzo, which uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows. The code is Cartesian, can be run in one, two, and three dimensions, and supports a wide variety of physics including hydrodynamics, ideal and non-ideal magnetohydrodynamics, N-body dynamics (and, more broadly, self-gravity of fluids and particles), primordial gas chemistry, optically thin radiative cooling of primordial and metal-enriched plasmas (as well as some optically-thick cooling models), radiation transport, cosmological expansion, and models for star formation and feedback in a cosmological context. In addition to explaining the algorithms implemented, we present solutions for a wide range of test problems, demonstrate the code's parallel performance, and discuss the Enzo collaboration's code development methodology.

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Enzo: An Adaptive Mesh Refinement Code for Astrophysics

Heusler ferromagnetic shape-memory alloys (FSMAs) normally consist of transition-group d-metals and main-group p-elements. Here, we report the realization of FSMAs in Heusler phases that completely consist of d metals. By introducing the d-metal Ti into NiMn alloys, cubic B2-type Heusler phase is obtained and the martensitic transformation temperature is decreased efficiently. Strong ferromagnetism is established by further doping Co atoms into the B2-type antiferromagnetic Ni-Mn-Ti austenite. Based on the magnetic-field-induced martensitic transformations, collective multifunctional properties are observed in Ni(Co)-Mn-Ti alloys. The d metals not only facilitate the formation of B2-type Heusler phases, but also establish strong ferromagnetic coupling and offer the possibility to tune the martensitic transformation.

Realization of multifunctional shape-memory ferromagnets in all-d-metal Heusler phases

Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. 
We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backwards optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network-related geoscience research.

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

Research on the use of autonomous vehicles as a mode of public transport in a city context is lacking. This paper focuses on the use of recently established autonomous buses (self-driving electric shuttle buses) running along a regular public transport line in a residential area of Oslo, Norway. We use a mixed-methods approach based on survey and interview data from two independent studies. The paper examines intentions to use autonomous buses before and after these were introduced in the case area as well as how passengers experience traveling by autonomous bus. Results show that the intention to use the autonomous buses was mostly positive both before and after using them. Most users felt safe while traveling by autonomous bus. Two suggestions for improvement made by the users were to: increase the speed and reduce the abrupt breaking of the autonomous buses. Overall, outcomes from this paper suggest that residents would be willing to use autonomous buses if these offer more frequent bus departures than the existing ones. However, as full automation has not been achieved yet and there is a host on board who can control the vehicle if necessary, passenger experiences and intentions to use should be reassessed with fully automated buses in future studies.

Autonomous buses: Intentions to use, passenger experiences, and suggestions for improvement

More advanced estimates of atmospheric conditions depend on merging the knowledge gained from humans and computers.

Meteorologists predict better weather forecasting with AI

Iridium marks the spot

Anthropogenic warming is reversing the predominant winds over the region and could explain the ice sheet’s destabilization.

https://physicstoday.scitation.org/doi/pdf/10.1063/PT.3.4311

Wind analysis links West Antarctic ice loss to humans

Better weather predictions and more data on driving could accelerate the widespread deployment of automated and connected vehicles.

https://physicstoday.scitation.org/doi/pdf/10.1063/PT.3.4362

Alex Lopatka

Papers

Meteorologists predict better weather forecasting with AI

Iridium marks the spot

Wind analysis links West Antarctic ice loss to humans

Self-driving cars face a cloudy future

NIST on a chip