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Resilient Forecasting of High-Dimensional Network Time Series in the Energy Domain: A Hybrid Approach
No full textEnergy systems are complex networks consisting of various interconnected components. Accurate energy demand and supply forecasts are crucial for efficient system operation and decision-making. However, high-dimensional data, complex network structures, and dynamic changes and disruptions in energy networks pose significant challenges for forecasting models. To address this, we propose a hybrid approach for resilient forecasting of network time series (HRF-NTS) in the energy domain. Our approach combines mathematical optimization methods with state-of-the-art machine learning techniques to achieve accurate and robust forecasts for high-dimensional energy network time series. We incorporate an optimization framework to account for uncertainties and disruptive changes in the energy system. The effectiveness of the proposed approach is demonstrated through a case study of forecasting energy demand and supply in a complex, large-scale natural gas transmission network. The results show that the hybrid approach outperforms alternative prediction models in terms of accuracy and resilience to structural changes and disruptions, providing stable, multi-step ahead forecasts for different short to mid-term forecasting horizons
A BDDC Preconditioner for the Cardiac EMI Model in three Dimensions
No full textWe analyze a Balancing Domain Decomposition by Constraints (BDDC)
preconditioner for the solution of three dimensional composite Discontinuous
Galerkin discretizations of reaction-diffusion systems of ordinary and partial
differential equations arising in cardiac cell-by-cell models like the
Extracellular space, Membrane and Intracellular space (EMI) Model. These
microscopic models are essential for the understanding of events in aging and
structurally diseased hearts which macroscopic models relying on homogenized
descriptions of the cardiac tissue, like Monodomain and Bidomain models, fail
to adequately represent. The modeling of each individual cardiac cell results
in discontinuous global solutions across cell boundaries, requiring the careful
construction of dual and primal spaces for the BDDC preconditioner. We provide
a scalable condition number bound for the precondition operator and validate
the theoretical results with extensive numerical experiments
On the Usability and Energy Efficiency of High-Level Synthesis for FPGA-based Network-Attached Accelerators
No full textHeterogeneity in high performance computing systems is one of the most promising approaches towards more energy-efficient computing on one hand and satisfying the raising demand of global computation capacity on the other hand. Besides the well-known key components like CPUs and GPGPUs are domain-specific accelerators like TPUs, FPGAs well known for their energy efficiency. This is especially true for highly specialized use cases. Network-attached accelerators promise more scalability and flexibility for FPGA usage in HPC environments. Easy and efficient programming of those accelerators is, however, still an open issue. Based on a framework for such accelerators which enables decoupling of FPGAs from their host system, we present a workflow using High-Level Synthesis (HLS) to offload application kernels to them. We evaluate this approach against a conventional Hardware Description Language (HDL) based workflow. In addition, we introduce the energy measurement tool EMA and assess the energy efficiency of both HLS and HDL design
Thermal environment and erosion of comet 67P/Churyumov-Gerasimenko
No full textAims. This paper focuses on how insolation affects the nucleus of comet 67P/Churyumov-Gerasimenko over its current orbit. We aim to better understand the thermal environment of the nucleus, in particular its surface temperature variations, erosion, relationship with topography, and how insolation affects the interior temperature for the location of volatile species (H2O and CO2).
Methods. We have developed two thermal models to calculate the surface and subsurface temperatures of 67P over its 6.45-year orbit. The first model, with high resolution (300 000 facets), calculates surface temperatures, taking shadows and self-heating into account but ignoring thermal conductivity. The second model, with lower resolution (10 000 facets), includes thermal conductivity to estimate temperatures down to ∼3 m below the surface.
Results. The thermal environment of 67P is strongly influenced by its large obliquity (52◦), which causes significant seasonal effects and polar nights. The northern hemisphere is the coldest region, with temperatures of 210–300 K. H2O is found in the first few centimetres, while CO2 is found deeper (∼2 m) except during polar night around perihelion, when CO2 accumulates near the surface. Cliffs erode 3–5 times faster than plains, forming terraces. The equatorial region receives maximum solar energy (8.5×109 J m−2 per orbit), with maximum surface temperatures of 300–350 K. On the plains, H2O is found in the first few centimetres, while CO2 is found deeper (∼2 m) and never accumulates near the surface. In the southern hemisphere, a brief intense perihelion heating raises temperatures to 350–400 K, which is followed by a 5-year polar night when surface temperatures drop to 55 K. Here H2O remains in the first few centimetres, while CO2 accumulates shallowly during polar night, enriching the region. Erosion is maximal in the southern hemisphere and concentrated on the plains, which explains the observed overall flatness of this hemisphere compared to the northern one. Over one orbit, the total energy from self-heating is 17% of the total energy budget, and 34% for thermal conduction. Our study contributes to a better understanding of the surface changes observed on 67P
Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
No full textWe explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. While warm-starting has been applied in this context before, a systematic methodology and analysis remain lacking. We address this gap by providing a theoretical characterization of warm-starting within scalarization methods, focusing on the sequencing of subproblems. However, optimizing the order of subproblems to maximize warm-start efficiency may conflict with alternative criteria, such as early identification of infeasible regions. We quantify these trade-offs through an extensive computational study
