FROSch Preconditioners for Land Ice Simulations of Greenland and Antarctica

Numerical simulations of Greenland and Antarctic ice sheets involve the solution of large-scale highly nonlinear systems of equations on complex shallow geometries. This work is concerned with the construction of Schwarz preconditioners for the solution of the associated tangent problems, which are challenging for solvers mainly because of the strong anisotropy of the meshes and wildly changing boundary conditions that can lead to poorly constrained problems on large portions of the domain. Here, two-level GDSW (Generalized Dryja–Smith–Widlund) type Schwarz preconditioners are applied to different land ice problems, i.e., a velocity problem, a temperature problem, as well as the coupling of the former two problems. We employ the MPI-parallel implementation of multi-level Schwarz preconditioners provided by the package FROSch (Fast and Robust Schwarz) from the Trilinos library. The strength of the proposed preconditioner is that it yields out-of-the-box scalable and robust preconditioners for the single physics problems. To our knowledge, this is the first time two-level Schwarz preconditioners are applied to the ice sheet problem and a scalable preconditioner has been used for the coupled problem. The preconditioner for the coupled problem differs from previous monolithic GDSW preconditioners in the sense that decoupled extension operators are used to compute the values in the interior of the subdomains. Several approaches for improving the performance, such as reuse strategies and shared memory OpenMP parallelization, are explored as well. In our numerical study we target both uniform meshes of varying resolution for the Antarctic ice sheet as well as non uniform meshes for the Greenland ice sheet are considered. We present several weak and strong scaling studies confirming the robustness of the approach and the parallel scalability of the FROSch implementation. Among the highlights of the numerical results are a weak scaling study for up to 32 K processor cores (8 K MPI-ranks and 4 OpenMP threads) and 566 M degrees of freedom for the velocity problem as well as a strong scaling study for up to 4 K processor cores (and MPI-ranks) and 68 M degrees of freedom for the coupled problem

Multifidelity Deep Operator Networks

Operator learning for complex nonlinear operators is increasingly common in modeling physical systems. However, training machine learning methods to learn such operators requires a large amount of expensive, high-fidelity data. In this work, we present a composite Deep Operator Network (DeepONet) for learning using two datasets with different levels of fidelity, to accurately learn complex operators when sufficient high-fidelity data is not available. Additionally, we demonstrate that the presence of low-fidelity data can improve the predictions of physics-informed learning with DeepONets

Late Holocene onset of intensive cultivation and introduction of the falaj irrigation system in the Salut oasis (Sultanate of Oman)

This paper discusses the time and steps of the introduction of intensive agriculture and evolution of irrigation systems to sustain crops in the palaeo-oasis of Salut in the northern Sultanate of Oman. Various geoarchaeological methods allow reconstructing the exploitation of the natural resources of the region and technological development of irrigation methods since the Mid-Holocene. Intensive agriculture started during the Bronze Age and continued with some spatial and intensity fluctuations up to the Islamic period. Cultivations were initially sustained by surface irrigation systems and later replaced by a dense net of aflaj, the typical surface/underground system adopted in the Levant, Arabian Peninsula and western Asia to collect water from deep piedmont aquifers and redistribute it to the fields located in the lowlands. Our results indicate that the aflaj were in use for a long period in the palaeo-oasis formed along Wadi Sayfam and surrounding the citadel of Salut. Uranium-Thorium dating of calcareous tufa formed in the underground tunnels of the aflaj suggests that they were used between ∼540 BCE and ∼1150 CE. After ∼1150 CE Wadi Sayfam were abandoned and the size of the oasis shrank substantially. During the late Islamic period, a surface aqueduct descending from the piedmont of Jabal Shams secured water supply. Our work confirms that in arid lands archaeological and historical communities were able to actively modulate their response to climate changes by using a variety of technological strategies

Electoral Predictions with Twitter: A Machine-Learning approach

Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art.Several studies have shown how to approximately predict public opinion, such as in political elections, by analyzing user activities in blogging platforms and on-line social networks. The task is challenging for several reasons. Sample bias and automatic understanding of textual content are two of several non trivial issues. In this work we study how Twitter can provide some interesting insights concerning the primary elections of an Italian political party. State-of-the-art approaches rely on indicators based on tweet and user volumes, often including sentiment analysis. We investigate how to exploit and improve those indicators in order to reduce the bias of the Twitter users sample. We propose novel indicators and a novel content-based method. Furthermore, we study how a machine learning approach can learn correction factors for those indicators. Experimental results on Twitter data support the validity of the proposed methods and their improvement over the state of the art

Geomorphology of the northwestern Kurdistan Region of Iraq: landscapes of the Zagros Mountains drained by the Tigris and Great Zab Rivers

We present the geomorphological map of the northwestern part of the Kurdistan Region of Iraq, where the landscape expresses the tectonic activity associated with the Arabia-Eurasia convergence and Neogene climate change. These processes influenced the evolution of landforms and fluvial pathways, where major rivers Tigris, Khabur, and Great Zab incise the landscape of Northeastern Mesopotamia Anticlinal ridges and syncline trough compose the Zagros orogen. The development of water and wind gaps, slope, and karsts processes in the highlands and the tilting of fluvial terraces in the flat areas are the main evidence of the relationship between tectonics, climate variations and geomorphological processes. During the Quaternary, especially after the Last Glacial Maximum, fluctuating arid and wet periods also influenced local landforms and fluvial patterns of the area. Finally, the intensified Holocene human occupation and agricultural activities during the passage to more complex societies over time impacted the evolution of the landscape in this part of Mesopotamia