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As the size and ubiquity of artificial intelligence and computational machine learning models grow, the energy required to train and use them is rapidly becoming economically and environmentally unsustainable. Recent laboratory prototypes of self-learning electronic circuits, such as “physical learning machines,” open the door to analog hardware that directly employs physics to learn desired functions from examples at a low energy cost. In this work, we show that this hardware platform allows for an even further reduction in energy consumption by using good initial conditions and a new learning algorithm. Using analytical calculations, simulations, and experiments, we show that a trade-off emerges when learning dynamics attempt to minimize both the error and the power consumption of the solution—greater power reductions can be achieved at the cost of decreasing solution accuracy. Finally, we demonstrate a practical procedure to weigh the relative importance of error and power minimization, improving the power efficiency given a specific tolerance to error. 

Classically, the quantity of contact area AR between two bodies is considered a proxy for the force of friction. However, bond density across the interface—quality of contact—is also relevant, and contemporary debate often centers around the relative importance of these two factors. In this work, we demonstrate that a third factor, often overlooked, plays a significant role in static frictional strength: The spatial distribution of contact. We perform static friction measurements, μ, on three pairs of solid blocks while imaging the contact plane. By using linear regression on hundreds of image-μ pairs, we are able to predict future friction measurements with three to seven times better accuracy than existing benchmarks, including total quantity of contact area. Our model has no access to quality of contact, and we therefore conclude that a large portion of the interfacial state is encoded in the spatial distribution of contact, rather than its quality or quantity 

Abstract Seismic moment and rupture length can be combined to infer stress drop, a key parameter for assessing earthquakes. In natural earthquakes, stress drops are largely depth‐independent, which is surprising given the expected dependence of frictional stress on normal stresses and hence overburden. We have developed a transparent experimental fault that allows direct observation of thousands of slip events, with ruptures that are fully contained within the fault. Surprisingly, the observed stress drops are largely independent of both the magnitude of normal stress and its heterogeneity, capturing the independence seen in nature. However, we observe larger, normal stress‐dependent stress drops when the fault area is reduced, which allows slip events to frequently reach the edge of the interface. We conclude that confined ruptures have normal stress independent stress drops, and thus the depth‐independent stress drops of tectonic earthquakes may be a consequence of their confined nature.