Journal of Computational Science最新文献

Constructing digital twins for predictive tumor treatment response models can have a high computational demand that presents a practical barrier for their clinical adoption. In this work, we demonstrate that proper orthogonal decomposition, by which a low-dimensional representation of the full model is constructed, can be used to dramatically reduce the computational time required to calibrate a partial differential equation model to magnetic resonance imaging (MRI) data for rapid predictions of tumor growth and response to chemotherapy. In the proposed formulation, the reduction basis is based on each patient’s own MRI data and controls the overall size of the “reduced order model”. Using the full model as the reference, we validate that the reduced order mathematical model can accurately predict response in 50 triple negative breast cancer patients receiving standard of care neoadjuvant chemotherapy. The concordance correlation coefficient between the full and reduced order models was 0.986 ± 0.012 (mean ± standard deviation) for predicting changes in both tumor volume and cellularity across the entire model family, with a corresponding median local error (inter-quartile range) of 4.36 % (1.22 %, 15.04 %). The total time to estimate parameters and to predict response dramatically improves with the reduced framework. Specifically, the reduced order model accelerates our calibration by a factor of (mean ± standard deviation) 378.4 ± 279.8 when compared to the full order model for a non-mechanically coupled model. This enormous reduction in computational time can directly help realize the practical construction of digital twins when the access to computational resources is limited.

The amalgamation of machine learning algorithms (ML) with computational fluid dynamics (CFD) represents a promising frontier for the advancement of fluid dynamics research. However, the practical integration of CFD with ML algorithms frequently faces challenges related to data transfer and computational efficiency. While CFD programs are conventionally scripted in Fortran or C/C++, the prevalence of Python in the machine learning domain complicates their seamless integration. To tackle these obstacles, this paper proposes a comprehensive solution. Our devised framework primarily leverages Python modules CFFI and dynamic linking library technology to seamlessly integrate ML algorithms with CFD programs, facilitating efficient data interchange between them. Distinguished by its simplicity, efficiency, flexibility, and scalability, our framework is adaptable across various CFD programs, scalable to multi-node parallelism, and compatible with heterogeneous computing systems. In this paper, we showcase a spectrum of CFD+ML algorithms based on this framework, including stability analysis of ML Reynolds stress models, bidirectional coupling between ML turbulence models and CFD programs, and online dimension reduction optimization techniques tailored for resolving unstable steady flow solutions. In addition, our framework has been successfully tested on supercomputer clusters, demonstrating its compatibility with distributed computing architectures and its ability to leverage heterogeneous computing resources for efficient computational tasks.

Multi-layer complex networks (MLCN) appears in various domains, such as, transportation, supply chains, etc. Failures in MLCN can lead to major disruptions in systems. Several research have focussed on different kinds of failures, such as, cascades, their reasons and ways to avoid them. This paper considers failures in a specific type of MLCN where the lower layer provides services to the higher layer without cross layer interaction, typical of a computer network. A three layer MLCN is constructed with the same set of nodes where each layer has different characteristics, the bottom most layer is Erdos–Renyi (ER) random graph with shortest path hop count among the nodes as gaussian, the middle layer is ER graph with higher number of edges from the previous, and the top most layer is preferential attachment graph with even higher number of edges. Both edge and node failures are considered. Failures happen with decreasing order of centralities of edges and nodes in static batch mode and when the centralities change dynamically with progressive failures. Emergent pattern of three key parameters, namely, average shortest path length (ASPL), total shortest path count (TSPC) and total number of edges (TNE) for all the three layers after node or edge failures are studied. Extensive simulations show that all but one parameters show definite degrading patterns. Surprising, ASPL for the middle layer starts showing a chaotic behaviour beyond a certain point for all types of failures.

In network function virtualization (NFV), Service Function Chaining (SFC) provides an ordered sequence of virtual network functions (VNFs) and subsequent steering of traffic flows through them to cater to end-to-end services. This paper addresses the NP-hard problem of minimum cost SFC deployment to support customer services that access the carrier network’s NFV infrastructure (NFVI) through some edge routers. To determine the mappings of VNFs to physical servers, a challenging aspect would be the inter-server latencies that may fluctuate over time because of the sharing nature of cloud data centers. To construct the SFC, we come up with three different formulations, each corresponding to a different informational assumption about the link latencies: First, a centralized integer linear programming (ILP) formulation is given under the assumption of the non-causal availability of exact and instantaneous inter-server latencies. The solution to this ILP can serve as a lower bound to benchmark more scalable and realistic schemes. Next, we give a distributed game-theoretic formulation (with service broker agents as players) which only requires the statistical knowledge of link latency fluctuations. The game provably admits a pure Nash equilibrium (NE) and can be solved iteratively through the well-known best response dynamics (BRD) algorithm. Our main novelty lies in the third formulation in which each service broker has neither instantaneous nor statistical knowledge of the latencies. Instead, it relies on a game-theoretic learning algorithm to compose its VNF chain only based on its own history of adopted decisions and experienced delays on each logical link. We prove that the proposed learning algorithm asymptotically converges to NE and evaluate its performance through simulations in terms of convergence and the impact of network parameters.

In healthcare, a remarkable progress in machine learning has given rise to a diverse range of predictive and decision-making medical models, significantly enhancing treatment efficacy and overall quality of care. These models often rely on electronic health records (EHRs) as fundamental data sources. The effectiveness of these models is contingent on the quality of the EHRs, typically presented as unstructured text. Unfortunately, these records frequently contain spelling errors, diminishing the quality of intelligent systems relying on them. In this research, we propose a method and a tool for correcting spelling errors in Russian medical texts. Our approach combines the Symmetrical Deletion algorithm with a finely tuned BERT model to efficiently correct spelling errors, thereby enhancing the quality of the original medical texts at a minimal cost. In addition, we introduce several fine-tuned BERT models for Russian anamneses. Through rigorous evaluation and comparison with existing spelling error correction tools for the Russian language, we demonstrate that our approach and tool surpass existing open-source alternatives by 7% in correcting spelling errors in sample Russian medical texts and significantly superior in automatically correcting real-world anamneses. However, the new approach is far inferior to proprietary services such as Yandex Speller and GPT-4. The proposed tool and its source code are available on GitHub ¹ and pip ² repositories. This paper is an extended version of the work presented at ICCS 2023 (Pogrebnoi et al. 2023)

Complex systems intricately intertwine with life, and the identification of the most influential spreaders in complex networks can aid in resolving numerous pragmatic problems. Nevertheless, the identification of such kinds of nodes currently stands as an open and challenging issue. In order to accurately and efficiently address this issue, numerous metrics have been proposed. In this paper, we propose a new method based on degree, clustering coefficient and k-shell decomposition value—$DCK$ to detect the most influential spreaders by gauging the spreading ability of nodes. The proposed centrality assesses the significance of a node by the impacts of its neighbors, encompassing both the local and global network structures. To evaluate the performance of $DCK$, we compare it with different centrality measures under utilizing the Susceptible–Infected–Recovered model to simulate the propagation of epidemics across real-world networks. Experiments on real networks illustrate that $DCK$ exhibits superior differentiation ability and more accurate identification ability for influential spreaders and compared with other methods, Kendall’s $\tau$ correlation coefficient of the $DCK$ could be enhanced by 12.82%, 13.20%, 8.62%, 5.32%, 7.97% and 11.73% for the degree centrality, K-shell decomposition, $GLI$ centrality, $H$-$GSM$ centrality, $LGI$ centrality and $NPCC$ centrality.

This paper presents a numerical approach to solve third and fourth order intego-differential equations (IDEs). In order to ascertain the numerical solution for third and fourth order IDEs of second kind, the newly introduced Higher order Haar wavelet method (HOHWM) has been employed to improve the numerical result and rate of convergence compared to classical Haar wavelet approach. Some examples available in the literature have been solved to verify the HOHWM’s effectiveness. To ensure that the approach presented is legitimate, applicable and achieves its objective, the maximum absolute error of each test problem is calculated at a test point.

Nonlinear initial / boundary value problems present challenges in solving due to the divergence of coefficients near singular points. This study introduces a novel hypergeometric wavelet-based approach designed to effectively address these equations. The specialized wavelet method efficiently manages singularities, resulting in improved accuracy. To evaluate the precision and effectiveness of this approach, Lane-Emden type problems are solved using the proposed methodology and compared against established benchmarks. Comparative analyses with alternative wavelet methods are conducted, featuring absolute error tables and graphical representations. The findings highlight the exceptional accuracy and efficiency of the proposed method relative to existing approaches. An advantage of this method is its requirement of fewer basis functions, leading to reduced computational time and complexity.

Regression of potential energy functions stands as one of the most prevalent applications of machine learning in the realm of materials simulation, offering the prospect of accelerating simulations by several orders of magnitude. Recently, graph-based architectures have emerged as particularly adept for modeling molecular systems. However, the development of robust and transferable potentials, leading to stable simulations for different sizes and physical conditions, remains an ongoing area of investigation. In this study, we compare the performance of several graph neural networks for predicting the energy of water cluster anions, a system of fundamental interest in Chemistry and Biology. Following the identification of the graph attention network as the optimal aggregation procedure for this task, we obtained an efficient and accurate energy model. This model is then employed to conduct Monte Carlo simulations of clusters across different sizes, demonstrating stable behavior. Notably, the predicted surface-to-interior state transition point and the bulk energy of the system are consistent with findings from other investigations, at a computational cost three-orders of magnitude lower.

The space fractional Klein–Gordon-Zakharov equations are transformed into the multi-symplectic structure system by introducing new auxiliary variables. The multi-symplectic system, which satisfies the multi-symplectic conservation, local energy and momentum conservation, is discretizated into the semi-discrete multi-symplectic system by the Fourier pseudo-spectral method. The second order multi-symplectic average vector field method is applied to the semi-discrete system. The fully discrete energy preserving scheme of the space fractional Klein–Gordon-Zakharov equation is obtained. Based on the composition method, a fourth order energy preserving scheme of the Riesz space fractional Klein–Gordon-Zakharov equations is also obtained. Numerical experiments confirm that these new schemes can have computing ability for a long time and can well preserve the discrete energy conservation property of the equations.