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Subsurface storage of CO2${\\mathrm{C}\\mathrm{O}}_{2}$is an important means to mitigate climate change, and the North Sea hosts considerable potential storage resources. To investigate the fate of CO2${\\mathrm{C}\\mathrm{O}}_{2}$over decades in vast reservoirs, numerical simulation based on realistic models is essential. Faults and other complex geological structures introduce modeling challenges as their effects on storage operations are subject to high uncertainty. We present a computational framework for forward propagation of uncertainty, including stochastic upscaling and copula representation of multivariate distributions for a CO2${\\mathrm{C}\\mathrm{O}}_{2}$storage site model with faults. The Vette fault zone in the Smeaheia formation in the North Sea is used as a test case. The stochastic upscaling method reduces the number of stochastic dimensions and the cost of evaluating the reservoir model. Copulas provide representation of dependent multidimensional random variables and a good fit to data, allow fast sampling and coupling to the forward propagation method via independent uniform random variables. The non‐stationary correlation within the upscaled flow functions are accurately captured by a data‐driven transformation model. The uncertainty in upscaled flow functions and other uncertain parameters are efficiently propagated to leakage estimates using numerical reservoir simulation of a two‐phase system of CO2 and brine. The expectations of leakage are estimated by an adaptive stratified sampling technique which effectively allocates samples in stochastic space. We demonstrate cost reduction compared to standard Monte Carlo of one or two orders of magnitude for simpler test cases, and factors 2–8 cost reduction for stochastic multi‐phase flow properties and more complex stochastic models. Plain Language Summary To limit global warming, greenhouse gases like CO2${\\mathrm{C}\\mathrm{O}}_{2}$can be injected into large reservoirs of porous rocks below the bottom of the sea instead of being emitted to the atmosphere. CO2${\\mathrm{C}\\mathrm{O}}_{2}$will slowly move in the reservoirs and may encounter faults, geological features that have properties that can either facilitate or stop the CO2 from moving further in the underground. It is important that the CO2 remains in the underground, and hence it is important to understand how it is affected by the fault, in particular when many physical rock properties are unknown due to very few or inexact measurements. We present methods to model the uncertainty in and surrounding the faults and show how more accurate computer simulations can be obtained by a combination of appropriate statistical models and adapted methods to investigate the effect of the fault uncertainty on the risk for leakage of CO2. Key Points Framework for efficient stochastic upscaling, modeling, and uncertainty propagation for CO2 storage, demonstrated on a North Sea test case Stochastic fault properties upscaled to two‐phase flow functions with reduced complexity and a format suitable for uncertainty propagation Significant computational cost reduction for adaptive stratified sampling compared to Monte Carlo sampling in estimation of CO2 leakage

Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide–MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide–MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide–MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.

Background Nanopore adaptive sampling (NAS) offers a promising approach for assessing genetic diversity in targeted genomic regions. Here we designed and validated an experiment to enrich a set of resistance genes in several melon cultivars as a proof of concept. Results Using the same reference to guide read acceptance or rejection with NAS, we successfully and accurately reconstructed the 15 regions in two newly assembled ssp. melo genomes and in a third ssp. agrestis cultivar. We obtained fourfold enrichment regardless of the tested samples, but with some variations according to the enriched regions. The accuracy of our assembly was further confirmed by PCR in the agrestis cultivar. We discussed parameters that could influence the enrichment and accuracy of NAS generated assemblies. Conclusions Overall, we demonstrated that NAS is a simple and efficient approach for exploring complex genomic regions, such as clusters of Nucleotide-binding site leucine-rich repeat (NLR) resistance genes. These regions are characterized by containing a high number of copy number variations, presence-absence polymorphisms and repetitive elements. These features make accurate assembly challenging but are crucial to study due to their central role in plant immunity and disease resistance. This approach facilitates resistance gene characterization in a large number of individuals, as required when breeding new cultivars suitable for the agroecological transition.

Surrogate modeling is of great practical significance for parametric differential equation systems. In contrast to classical numerical methods, using physics-informed deep learning-based methods to construct simulators for such systems is a promising direction due to its potential to handle high dimensionality, which requires minimizing a loss over a training set of random samples. However, the random samples introduce statistical errors, which may become the dominant errors for the approximation of low-regularity and high-dimensional problems. In this work, we present a deep adaptive sampling method for surrogate modeling of low-regularity parametric differential equations and illustrate the necessity of adaptive sampling for constructing surrogate models. In the parametric setting, the residual loss function can be regarded as an unnormalized probability density function (PDF) of the spatial and parametric variables. In contrast to the non-parametric setting, factorized joint density models can be employed to alleviate the difficulties induced by the parametric space. The PDF is approximated by a deep generative model, from which new samples are generated and added to the training set. Since the new samples match the residual-induced distribution, the refined training set can further reduce the statistical error in the current approximate solution through variance reduction. We demonstrate the effectiveness of the proposed method with a series of numerical experiments, including the physics-informed operator learning problem, the parametric optimal control problem with geometrical parametrization, and the parametric lid-driven 2D cavity flow problem with a continuous range of Reynolds numbers from 100 to 3200.

Physics-informed neural networks (PINNs) have garnered widespread use for solving a variety of complex partial differential equations (PDEs). Nevertheless, when addressing certain specific problem types, traditional sampling algorithms still reveal deficiencies in efficiency and precision. In response, this paper builds upon the progress of adaptive sampling techniques, addressing the inadequacy of existing algorithms to fully leverage the spatial location information of sample points, and introduces an innovative adaptive sampling method. This approach incorporates the Dual Inverse Distance Weighting (DIDW) algorithm, embedding the spatial characteristics of sampling points within the probability sampling process. Furthermore, it introduces reward factors derived from reinforcement learning principles to dynamically refine the probability sampling formula. This strategy more effectively captures the essential characteristics of PDEs with each iteration. We utilize sparsely connected networks and have adjusted the sampling process, which has proven to effectively reduce the training time. In numerical experiments on fluid mechanics problems, such as the two-dimensional Burgers’ equation with sharp solutions, pipe flow, flow around a circular cylinder, lid-driven cavity flow, and Kovasznay flow, our proposed adaptive sampling algorithm markedly enhances accuracy over conventional PINN methods, validating the algorithm’s efficacy.

In this paper, a compressed adaptive image-sensing method based on an overcomplete ridgelet dictionary is proposed. Some low-complexity operations are designed to distinguish between smooth blocks and texture blocks in the compressed domain, and adaptive sampling is performed by assigning different sampling rates to different types of blocks. The efficient, sparse representation of images is achieved by using an overcomplete ridgelet dictionary; at the same time, a reasonable dictionary-partitioning method is designed, which effectively reduces the number of candidate dictionary atoms and greatly improves the speed of classification. Unlike existing methods, the proposed method does not rely on the original signal, and computation is simple, making it particularly suitable for scenarios where a device’s computing power is limited. At the same time, the proposed method can accurately identify smooth image blocks and more reasonably allocate sampling rates to obtain a reconstructed image with better quality. The experimental results show that our method’s image reconstruction quality is superior to that of existing ARCS methods and still maintains low computational complexity.

The sample distribution has a vital influence on the quality of a Kriging surrogate model, which may further influence the required cost or convergence of the surrogate model-based design and optimization problems. Adaptive sampling methods utilize the information from existing samples to reasonably allocate the sequential samples, which can generally build a more accurate Kriging surrogate model under the same computational budget. However, most of the existing adaptive sampling methods for the Kriging surrogate model are only available for single-output problems, and there are few studies on problems with multiple responses. In this paper, an adaptive sampling method based on Delaunay triangulation and technique for order preference by similarity to ideal solution (TOPSIS) is proposed for Kriging surrogate model with multiple outputs (mKMDT). In the proposed mKMDT, Delaunay triangulation is used to partition the design space into multiple triangle regions, whose area denotes the dispersion of the sample points. The prediction error at each triangle’s centroid represents the local approximation error. Specifically, three different strategies are developed when allocating weights to the area and the prediction error of each triangle with the entropy method and the TOPSIS method. The performance of the proposed method is illustrated through numerical examples with different numbers of outputs and a collision problem between the missile and the adapter. Results show that the proposed method can construct an accuracy surrogate model with few samples, which is useful for practical engineering design problems with multiple outputs.

Wireless Body Area Networks (WBANs) have received a lot of attention due to various medical and non-medical applications. However, the sensor energy remains a limitation for the lifetime of WBANs and eventually providing sustainable services. The highest amount of energy consumption is related to the sampling operation of the sensors. Therefore, reducing the sampling rate is the key solution to extend network lifetime. Though, existing sampling algorithms have two issues: (1) Existing methods increase energy consumption through unnecessary data sampling and (2) are a way of energy saving and cannot guarantee self-sustainability of sensors. Therefore, a Dynamic Sampling method based on Change Rate (DSCR) for energy-harvesting body nodes is proposed in this paper to address these two problems. Each node in DSCR is equipped with an adaptive energy manager. The node uses different methods for determining the sampling rate based on the energy level of sensor and the quality of energy harvesting in order to make the WBANs energy neutral. The energy manager in DSCR classifies the sensors into three classes A, B, and C in terms of the level of energy. The sampling rate of each class is determined independently. The simulations show that, compared to the state-the-art methods, the proposed method can reduce the sampling rate by 50.84% and data overhead by 78% on average while conserving data integrity.

Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remains challenging, often leading to limited accuracy and efficiency. In this work, we introduce a hybrid adaptive sampling and weighting method to enhance the performance of physics-informed neural networks (PINNs). The adaptive sampling component identifies training points in regions where the solution exhibits rapid variation, while the adaptive weighting component balances the convergence rate across training points. Numerical experiments show that applying only adaptive sampling or only adaptive weighting is insufficient to consistently achieve accurate predictions, particularly when training points are scarce. Since each method emphasizes different aspects of the solution, their effectiveness is problem dependent. By combining both strategies, the proposed framework consistently improves prediction accuracy and training efficiency, offering a more robust approach for solving PDEs with PINNs.

Multi-fidelity surrogate (MFS) method is very promising for the optimization of complex problems. The optimization capability of MFS can be improved by infilling samples in the optimization process. Furthermore, once the gradient information is provided, the gradient-enhanced kriging (GEK) can be utilized to construct a more accurate MFS model. However, for the existing infill sampling criterions, it is difficult to improve the optimization speed without sacrificing the optimization gains. In this paper, a novel infill sampling criterion named Adaptive Multi-fidelity Expected Improvement (AMEI) is proposed, in which the prediction accuracy and the optimization potential of the surrogate model are both considered. With a set of extra samples calculated, the AMEI determines which fidelity model for the new sample is to be added. Through two numerical examples and two engineering examples, it can be found that the AMEI always provides the best optimization result with the fewest analysis calls, and the robustness is also good. The optimization capability and efficiency of the AMEI have been demonstrated compared with traditional criterions.