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  1. Free, publicly-accessible full text available January 1, 2024
  2. Accurate predictions of water temperature are the foundation for many decisions and regulations, with direct impacts on water quality, fishery yields, and power production. Building accurate broad-scale models for lake temperature prediction remains challenging in practice due to the variability in the data distribution across different lake systems monitored by static and time-series data. In this paper, to tackle the above challenges, we propose a novel machine learning based approach for integrating static and time-series data in deep recurrent models, which we call Invertibility-Aware-Long Short-Term Memory(IA-LSTM), and demonstrate its effectiveness in predicting lake temperature. Our proposed method integrates components of the Invertible Network and LSTM to better predict temperature profiles (forward modeling) and infer the static features (i.e., inverse modeling) that can eventually enhance the prediction when static variables are missing. We evaluate our method on predicting the temperature profile of 450 lakes in the Midwestern U.S. and report a relative improvement of 4\% to capture data heterogeneity and simultaneously outperform baseline predictions by 12\% when static features are unavailable. 
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  3. Abstract

    Most Upper Jurassic studies of astronomical forcing have focused on deeper‐water sections which are relatively continuous. An Upper Jurassic (Kimmeridgian) section on the greenhouse Adriatic Carbonate Platform, Croatia, was studied to determine if astronomical forcing can be recognized in a 5.8 ± 0.1 Myr duration, disconformity‐prone shallow platform succession. The succession consists of metre‐scale subtidal parasequences intermixed with peritidal parasequences, and intermittent subaerial breccias at sequence boundaries. Ages were constrained by biostratigraphy and δ13C chemostratigraphy, and most sequence boundaries appear to match those of the coastal onlap curve of Haq (2018). Logged sections were converted into depth–rank time series and parasequence–thickness time series. Accumulation rates were statistically evaluated for the rank series against an astronomical‐forcing model, and compared with long‐term accumulation rates (thickness divided by time). The statistical rates were used to select theca100 kyr eccentricity cycle to tune the series. Spectral analysis showed peaks atca400 kyr (superbundles) andca100 kyr (bundles), along with obliquity (38 kyr and 27 kyr) and precessional (18−22 kyr) cycles (parasequences). The Kimmeridgian sequences areca400 kyr,ca800 kyr andca1.1 Myr duration. Sequence scale (0.4 to 1.2 Myr) stratigraphic completeness based on statistical accumulation rates versus long‐term rates isca60%. This study estimatesca1 Myr missing time in parasequences stacked into superbundles and 1.6 Myr in four major sequence boundaries. Given that the Kimmeridgian was the hottest time of the Middle and Late Jurassic, aquifer eustasy may have influenced the timing of sequence boundaries, although documented late Kimmeridgian cooling could have triggered a glacio‐eustatic component.

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  4. Demeniconi, Carlotta ; Davidson, Ian (Ed.)
    This paper proposes a physics-guided machine learning approach that combines machine learning models and physics-based models to improve the prediction of water flow and temperature in river networks. We first build a recurrent graph network model to capture the interactions among multiple segments in the river network. Then we transfer knowledge from physics-based models to guide the learning of the machine learning model. We also propose a new loss function that balances the performance over different river segments. We demonstrate the effectiveness of the proposed method in predicting temperature and streamflow in a subset of the Delaware River Basin. In particular, the proposed method has brought a 33%/14% accuracy improvement over the state-of-the-art physics-based model and 24%/14% over traditional machine learning models (e.g., LSTM) in temperature/streamflow prediction using very sparse (0.1%) training data. The proposed method has also been shown to produce better performance when generalized to different seasons or river segments with different streamflow ranges. 
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  5. Abstract

    The detection of GW170817, the first neutron star-neutron star merger observed by Advanced LIGO and Virgo, and its following analyses represent the first contributions of gravitational wave data to understanding dense matter. Parameterizing the high density section of the equation of state of both neutron stars through spectral decomposition, and imposing a lower limit on the maximum mass value, led to an estimate of the stars’ radii ofkm andkm (Abbottet al2018Phys. Rev. Lett.121161101). These values do not, however, take into account any uncertainty owed to the choice of the crust low-density equation of state, which was fixed to reproduce the SLy equation of state model (Douchin and Haensel 2001Astron. Astrophys.380151). We here re-analyze GW170817 data and establish that different crust models do not strongly impact the mass or tidal deformability of a neutron star—it is impossible to distinguish between low-density models with gravitational wave analysis. However, the crust does have an effect on inferred radius. We predict the systematic error due to this effect using neutron star structure equations, and compare the prediction to results from full parameter estimation runs. For GW170817, this systematic error affects the radius estimate by 0.3 km, approximatelyof the neutron stars’ radii.

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  6. This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. Specifically, we show that a PGRNN can improve prediction accuracy over that of physical models, while generating outputs consistent with physical laws, and achieving good generalizability. Standard RNNs, even when producing superior prediction accuracy, often produce physically inconsistent results and lack generalizability. We further enhance this approach by using a pre-training method that leverages the simulated data from a physics-based model to address the scarcity of observed data. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines where mechanistic (also known as process-based) models are used, e.g., power engineering, climate science, materials science, computational chemistry, and biomedicine. 
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  7. We construct a notion of teleparallelization for Newton–Cartan theory, and show that the teleparallel equivalent of this theory is Newtonian gravity; furthermore, we show that this result is consistent with teleparallelization in general relativity, and can be obtained by null-reducing the teleparallel equivalent of a five-dimensional gravitational wave solution. This work thus strengthens substantially the connections between four theories: Newton–Cartan theory, Newtonian gravitation theory, general relativity, and teleparallel gravity. 
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