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Abstract Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation, including computational fluid dynamics (CFD), is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics, such as temperature, velocity, and pressure, without using any training data on velocity and pressure. This approach avoids solving the nonlinear Navier–Stokes equation numerically, which significantly reduces the computational cost (if including the cost of velocity data generation). The difficult-to-determine parameters' values of the governing equations can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the extra penalty by incorporating governing PDEs, initial conditions, and boundary conditions in the PINN model. 

We present results of a search for spin-independent dark matter-nucleus interactions in a $1{\mathrm{cm}}^2$by 1 mm thick (0.233 g) high-resolution silicon athermal phonon detector operated above ground. For interactions in the substrate, this detector achieves an rms baseline energy resolution of $361.5\left(4\right)\mathrm{m}\mathrm{eV}$(statistical error), the best for any athermal phonon detector to date. With an exposure of $0.233\mathrm{g}\times 12$hours, we place the most stringent constraints on dark matter masses between 44 and $87\mathrm{M}\mathrm{eV}/{\mathrm{c}}^2$, with the lowest unexplored cross section of $4\times {10}^{-32}{\mathrm{c}\mathrm{m}}^2$at $87\mathrm{M}\mathrm{eV}/{\mathrm{c}}^2$. We employ a conservative salting technique to reach the lowest dark matter mass ever probed via direct detection experiment. This constraint is enabled by two-channel rejection of low energy backgrounds that are coupled to individual sensors. 

Data and R script repository for the manuscript to reproduce figs 5 and 6; Deglacial temperature and carbonate saturation state variability in the tropical Atlantic at Antarctic Intermediate Water Depths" (Oppo et al., 2023) 

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Paleoceanography. The data include parameters of paleocean (oxygen isotopes) with a geographic location of North Atlantic Ocean. The time period coverage is from 22423 to 563 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data. 

Abstract Variations in the Atlantic Meridional Overturning Circulation (AMOC) redistribute heat and nutrients, causing pronounced anomalies of temperature and nutrient concentrations in the subsurface ocean. However, exactly how millennial‐scale deglacial AMOC variability influenced the subsurface is debated, and the role of other deglacial forcings of subsurface temperature change is unclear. Here, we present a new deglacial temperature reconstruction, which, with published records, helps assess competing hypotheses for deglacial warming in the upper tropical North Atlantic. Our record provides new evidence of regional subsurface warming in the western tropical North Atlantic within the core of modern Antarctic Intermediate Water (AAIW) during Heinrich Stadial 1 (HS1), an early deglacial interval of iceberg discharge into the North Atlantic. Our results are consistent with model simulations that suggest subsurface heat accumulates in the northern high‐latitude convection regions and along the upper AMOC return path when the AMOC weakens, and with warming due to rising greenhouse gases. Warming of AAIW may have also contributed to warming in the tropics at modern AAIW depths during late HS1. Nutrient andreconstructions from the same site suggest a link between AMOC intensity and the northward extent of AAIW in the northern tropics across the deglaciation and on millennial time scales. However, the timing of the initial deglacial increase in AAIW to the northern tropics is ambiguous. Deglacial trends and variability ofin the upper North Atlantic have likely biased temperature reconstructions based on the elemental composition of calcitic benthic foraminifera.