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The system of oceanic flows constituting the Atlantic Meridional Overturning Circulation (AMOC) moves heat and other properties to the subpolar North Atlantic, controlling regional climate, weather, sea levels, and ecosystems. Climate models suggest a potential AMOC slowdown towards the end of this century due to anthropogenic forcing, accelerating coastal sea level rise along the western boundary and dramatically increasing flood risk. While direct observations of the AMOC are still too short to infer long-term trends, we show here that the AMOC-induced changes in gyre-scale heat content, superimposed on the global mean sea level rise, are already influencing the frequency of floods along the United States southeastern seaboard. We find that ocean heat convergence, being the primary driver for interannual sea level changes in the subtropical North Atlantic, has led to an exceptional gyre-scale warming and associated dynamic sea level rise since 2010, accounting for 30-50% of flood days in 2015-2020.

 

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec’s simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning.

 

We develop a set of highly efficient and effective computational algorithms and simulation tools for fluid simulations on a network. The mathematical models are a set of hyperbolic conservation laws on edges of a network, as well as coupling conditions on junctions of a network. For example, the shallow water system, together with flux balance and continuity conditions at river intersections, model water flows on a river network. The computationally accurate and robust discontinuous Galerkin methods, coupled with explicit strong stability preserving Runge-Kutta methods, are implemented for simulations on network edges. Meanwhile, linear and nonlinear scalable Riemann solvers are being developed and implemented at network vertices. These network simulations result in tools that are added to the existing PETSc and DMNetwork software libraries for the scientific community in general. Simulation results of a shallow water system on a Mississippi river network with over one billion network variables are performed on an extreme-scale computer using up to 8,192 processor with an optimal parallel efficiency. Further potential applications include traffic flow simulations on a highway network and blood flow simulations on a arterial network, among many others. 

Earth’s inner core is predominantly composed of solid iron (Fe) and displays intriguing properties such as strong shear softening and an ultrahigh Poisson’s ratio. Insofar, physical mechanisms to explain these features coherently remain highly debated. Here, we have studied longitudinal and shear wave velocities of hcp-Fe (hexagonal close-packed iron) at relevant pressure–temperature conditions of the inner core using in situ shock experiments and machine learning molecular dynamics (MLMD) simulations. Our results demonstrate that the shear wave velocity of hcp-Fe along the Hugoniot in the premelting condition, defined asT/T_m(T_m: melting temperature of iron) above 0.96, is significantly reduced by ~30%, while Poisson’s ratio jumps to approximately 0.44. MLMD simulations at 230 to 330 GPa indicate that collective motion with fast diffusive atomic migration occurs in premelting hcp-Fe primarily along [100] or [010] crystallographic direction, contributing to its elastic softening and enhanced Poisson’s ratio. Our study reveals that hcp-Fe atoms can diffusively migrate to neighboring positions, forming open-loop and close-loop clusters in the inner core conditions. Hcp-Fe with collective motion at the inner core conditions is thus not an ideal solid previously believed. The premelting hcp-Fe with collective motion behaves like an extremely soft solid with an ultralow shear modulus and an ultrahigh Poisson’s ratio that are consistent with seismic observations of the region. Our findings indicate that premelting hcp-Fe with fast diffusive motion represents the underlying physical mechanism to help explain the unique seismic and geodynamic features of the inner core.

 

The SNP-set analysis is a powerful tool for dissecting the genetics of complex human diseases. There are three fundamental genetic association approaches to SNR-set analysis: the marginal model fitting approach, the joint model fitting approach, and the decorrelation approach. A problem of primary interest is how these approaches compare with each other. To address this problem, we develop a theoretical platform to compare the signal-to-noise ratio (SNR) of these approaches under the generalized linear model. We elaborate on how causal genetic effects give rise to statistically detectable association signals, and show that when causal effects spread over blocks of strong linkage disequilibrium (LD), the SNR of the marginal model fitting is usually higher than that of the decorrelation approach, which in turn is higher than that of the unbiased joint model fitting approach. We also scrutinize dense effects and LDs by a bivariate model and extensive simulations using the 1000 Genome Project data. Last, we compare the statistical power of two generic types of SNP-set tests (summation-based and supremum-based) by simulations and an osteoporosis study using large data from UK Biobank. Our results help develop powerful tools for SNP-set analysis and understand the signal detection problem in the presence of colored noise. 

Digibox is a prototyping environment for IoT applications. It enables a novel scene-centric prototyping where developers can program an ensemble of simulated devices to capture not only their individual but also their coordinated behaviors, making it possible to test, debug, and evaluate the behaviors of an IoT application. Using Digibox, developers can download and reuse existing scenes, customize, and repurpose them towards developing new applications; or replicate others' experiment results from scientific research. Digibox's Kubernetes-based runtime further allows developers to easily scale the prototyping environment from a single laptop to a cluster running simulated devices and scenes at a scale appropriate to the application.