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        <title>Frontiers in Physics | Computational Physics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/physics/sections/computational-physics</link>
        <description>RSS Feed for Computational Physics section in the Frontiers in Physics journal | New and Recent Articles</description>
        <language>en-us</language>
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        <pubDate>2026-04-24T14:07:49.436+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2025.1587012</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2025.1587012</link>
        <title><![CDATA[Adaptive biases-incorporated latent factorization of tensors for predicting missing data in water quality monitoring networks]]></title>
        <pubdate>2025-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xuke Wu</author><author>Lan Wang</author><author>Miao Ge</author><author>Jing Jiang</author><author>Yu Cai</author><author>Bing Yang</author>
        <description><![CDATA[Real-time monitoring of key water quality parameters is essential for the scientific management and effective maintenance of aquatic ecosystems. Water quality monitoring networks equipped with multiple low-cost electrochemical and optical sensors generate abundant spatiotemporal data for water authorities. However, large-scale missing data in wireless sensor networks is inevitable due to various factors, which may introduce uncertainties in downstream mathematical modeling and statistical decisions, potentially leading to misjudgments in water quality risk assessment. A high-dimensional and incomplete (HDI) tensor can specifically quantify multi-sensor data, and latent factorization of tensors (LFT) models effectively extract multivariate dependencies and spatiotemporal correlations hidden in such a tensor to achieve high-accuracy missing data imputation. Nevertheless, LFT models fail to adequately account for the inherent fluctuations in water quality data, limiting their representation learning ability. Empirical evidence suggests that incorporating bias schemes into learning models can effectively mitigate underfitting. Building on this insight, this study proposes an adaptive biases-incorporated LFT (ABL) model with four-fold ideas: basic linear biases to describe constant fluctuations in water quality data; weighted pretraining biases to capture historical prior information of data fluctuations; time-aware biases to model long-term patterns of water quality fluctuations; and hyperparameter adaptation via particle swarm optimization (PSO) to enhance practicality. Empirical studies on large-scale real-world water quality datasets demonstrate that the proposed ABL model achieves significant improvements in both prediction accuracy and computational efficiency compared with state-of-the-art models. The findings highlight that integrating multiple bias schemes into tensor factorization models can effectively address the limitations of existing LFT models in capturing inherent data fluctuations, thereby enhancing the reliability of missing data imputation for water quality monitoring. This advancement contributes to more robust downstream applications in water quality management and risk assessment.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2021.555517</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2021.555517</link>
        <title><![CDATA[Construction of C1 Rational Bi-Quartic Spline With Positivity-Preserving Interpolation: Numerical Results and Analysis]]></title>
        <pubdate>2021-08-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Samsul Ariffin Abdul Karim</author><author>Azizan Saaban</author><author>Van Thien Nguyen</author>
        <description><![CDATA[From the observed datasets, we should be able to produce curve surfaces that have the same characteristics as the original datasets. For instance, if the given data are positive, then the resulting curve or surface must be positive on entire given intervals, i.e., everywhere. In this study, a new partial blended rational bi-quartic spline with C1 continuity is constructed through the partially blended scheme. This rational spline is defined on four corners of the rectangular meshes. The sufficient condition for the positivity of rational bi-quartic spline is derived on four boundary curve networks. There are eight free parameters that can be used for shape modification. The first-order partial derivatives are estimated by using numerical techniques. We also show that the proposed scheme is local quadratic reproducing such that it can exactly reproduce the quadratic surface. We test the proposed scheme to interpolate various types of positive surface data. Based on statistical indicators such as the root mean square error (RMSE) and coefficient of determination (R2), we found that the proposed scheme is on par with some established schemes. In fact, it requires less CPU times (in seconds) to generate the interpolating surface on rectangular meshes. Furthermore, by combining the statistical indicators’ result and graphically visualizing the test functions, the proposed method has the capability to reconstruct very comparable smoothing interpolating positive surfaces compared to some existing schemes. This finding is significant in producing a better interpolating surface for computer graphics applications since the proposed scheme has a smaller error compared with existing schemes.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.548497</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.548497</link>
        <title><![CDATA[Fluid Meniscus Algorithms for Dynamic Pore-Network Modeling of Immiscible Two-Phase Flow in Porous Media]]></title>
        <pubdate>2021-03-11T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Santanu Sinha</author><author>Magnus Aa. Gjennestad</author><author>Morten Vassvik</author><author>Alex Hansen</author>
        <description><![CDATA[We present in detail a set of algorithms for a dynamic pore-network model of immiscible two-phase flow in porous media to carry out fluid displacements in pores. The algorithms are universal for regular and irregular pore networks in two or three dimensions and can be applied to simulate both drainage displacements and steady-state flow. They execute the mixing of incoming fluids at the network nodes, then distribute them to the outgoing links and perform the coalescence of bubbles. Implementing these algorithms in a dynamic pore-network model, we reproduce some of the fundamental results of transient and steady-state two-phase flow in porous media. For drainage displacements, we show that the model can reproduce the flow patterns corresponding to viscous fingering, capillary fingering and stable displacement by varying the capillary number and viscosity ratio. For steady-state flow, we verify non-linear rheological properties and transition to linear Darcy behavior while increasing the flow rate. Finally we verify the relations between seepage velocities of two-phase flow in porous media considering both disordered regular networks and irregular networks reconstructed from real samples.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.500690</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.500690</link>
        <title><![CDATA[A Novel Robust Strategy for Discontinuous Galerkin Methods in Computational Fluid Mechanics: Why? When? What? Where?]]></title>
        <pubdate>2021-01-29T00:00:00Z</pubdate>
        <category>Review</category>
        <author>Gregor J. Gassner</author><author>Andrew R. Winters</author>
        <description><![CDATA[In this paper we will review a recent emerging paradigm shift in the construction and analysis of high order Discontinuous Galerkin methods applied to approximate solutions of hyperbolic or mixed hyperbolic-parabolic partial differential equations (PDEs) in computational physics. There is a long history using DG methods to approximate the solution of partial differential equations in computational physics with successful applications in linear wave propagation, like those governed by Maxwell’s equations, incompressible and compressible fluid and plasma dynamics governed by the Navier-Stokes and the Magnetohydrodynamics equations, or as a solver for ordinary differential equations (ODEs), e.g., in structural mechanics. The DG method amalgamates ideas from several existing methods such as the Finite Element Galerkin method (FEM) and the Finite Volume method (FVM) and is specifically applied to problems with advection dominated properties, such as fast moving fluids or wave propagation. In the numerics community, DG methods are infamous for being computationally complex and, due to their high order nature, as having issues with robustness, i.e., these methods are sometimes prone to crashing easily. In this article we will focus on efficient nodal versions of the DG scheme and present recent ideas to restore its robustness, its connections to and influence by other sectors of the numerical community, such as the finite difference community, and further discuss this young, but rapidly developing research topic by highlighting the main contributions and a closing discussion about possible next lines of research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.593275</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.593275</link>
        <title><![CDATA[On Topological Indices of mth Chain Hex-Derived Network of Third Type]]></title>
        <pubdate>2020-11-20T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Yuhong Huo</author><author>Haidar Ali</author><author>Muhammad Ahsan Binyamin</author><author>Syed Sheraz Asghar</author><author>Usman Babar</author><author>Jia-Bao Liu</author>
        <description><![CDATA[In theoretical chemistry, the numerical parameters that are used to characterize the molecular topology of graphs are called topological indices. Several physical and chemical properties like boiling point, entropy, heat formation, and vaporization enthalpy of chemical compounds can be determined through these topological indices. Graph theory has a considerable use in evaluating the relation of various topological indices of some derived graphs. In this article, we will compute the topological indices like Randić, first Zagreb, harmonic, augmented Zagreb, atom-bond connectivity, and geometric-arithmetic indices for chain hex-derived network of type 3 CHDN3(m,n) for different cases of m and n. We will also compute the numerical computation and graphical view to justify our results.Mathematics Subject Classification: 05C12, 05C90]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.547963</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.547963</link>
        <title><![CDATA[SVM-Based Multi-Dividing Ontology Learning Algorithm and Similarity Measuring on Topological Indices]]></title>
        <pubdate>2020-10-23T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Linli Zhu</author><author>Gang Hua</author><author>Haci Mehmet Baskonus</author><author>Wei Gao</author>
        <description><![CDATA[Ontology is one of the oldest terminologies in physics and is used to describe the origin and most essential attributes of all things in the world. With the development of contemporary science, ontology was given a specific definition and then introduced into the computer science as a conceptual model to describe the relationship between objects. In the past decade, the algorithms and applications in the ontology-related field have attracted the attention of many scholars. In this work, a support vector machines based multi-dividing ontology learning algorithm is proposed. We pay attention to the similarity of topological indices in chemical graph theory, and apply SVM-based multi-dividing ontology learning algorithms to give some calculation results of similarity between topological indices.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.587419</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.587419</link>
        <title><![CDATA[Tunable Magnetic Anisotropy and Dzyaloshinskii-Moriya Interaction in an Ultrathin van der Waals Fe3GeTe2/In2Se3 Heterostructure]]></title>
        <pubdate>2020-09-25T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Dong Chen</author><author>Wei Sun</author><author>Hang Li</author><author>Jianli Wang</author><author>Yuanxu Wang</author>
        <description><![CDATA[The promise of future spintronic devices with nanoscale dimension, high-density, and low-energy consumption motivates the search for van der Waals heterostructure that stabilize topologically protected whirling spin textures such as magnetic skyrmions and domain walls. To translate these compelling features into practical devices, a key challenge lies in achieving effective manipulation of the magnetic anisotropy energy and the Dzyaloshinskii-Moriya (DM) interaction, the two key parameters that determine skyrmions. Through the first-principles calculation, we demonstrate that the polarization-induced broken inversion symmetry in the two-dimensional Fe3GeTe2/In2Se3 multiferroic heterostructure does cause an interfacial DM interaction. The strong spin-orbit coupling triggers the magnetic anisotropy of the Fe3GeTe2/In2Se3 heterostructure. The magnetic anisotropy and the DM interaction in Fe3GeTe2 can be well-controlled by the ferroelectric polarization of In2Se3. This work paves the way toward the spintronic devices based on van der Waals heterostructures.PACS: 63.20.dk, 74.78.Fk, 85.75.-d, 75.30.Gw]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00219</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00219</link>
        <title><![CDATA[Experimental Investigation, Modeling, and Optimization of Wear Parameters of B4C and Fly-Ash Reinforced Aluminum Hybrid Composite]]></title>
        <pubdate>2020-07-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Mohit Kumar Sahu</author><author>Raj Kumar Sahu</author>
        <description><![CDATA[Lightweight and high-wear performance materials are currently in demand for various advanced applications in areas such as aerospace and automobiles. These demands can be achieved by hybrid aluminum matrix composites (HAMCs), as they possess excellent mechanical and tribological properties which can be customized using more than one reinforcement. Boron carbide (8 wt.%) and fly-ash (2 wt.%) reinforced hybrid aluminum 7075 composite was successfully fabricated using a stir-casting route. Wear is a crucible phenomenon that occurs over the interaction of surfaces and affects the performance of the material. To investigate wear behavior of developed HAMC, dry sliding wear tests were conducted based on the central composite design, taking the specific wear rate as a response parameter. Modeling of wear parameters is crucial, as it helps to predict the value of the wear response at the given set of input parameters without performing experimentation. Response surface method (RSM) was used for the modeling of wear parameters to develop an empirical model of specific wear rate in terms of load, sliding speed, and sliding distance. The high value of the coefficient of determination (R2 = 0.9894) illustrates the goodness of fit of the developed model. Moreover, the optimal condition of wear parameters was determined as 20 N load, 1.5 m/s sliding speed, and 500 m sliding distance; the predicted value of specific wear rate in this set of parameters is 0.2 × 10−5 mm3/N-m. The validation test at optimal conditions was performed and the specific wear rate was found to be 0.205 × 10−5 mm3/N-m, which shows good agreement with the predicted value. The worn-out surface and debris were analyzed using scanning electron microscope (SEM) images and electron dispersive spectrums (EDS) to completely explore the mechanism of wear.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00232</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00232</link>
        <title><![CDATA[GGA and GGA Plus U Study of Half-Metallic Quaternary Heusler Compound CoCrScSn]]></title>
        <pubdate>2020-06-30T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chuankun Zhang</author><author>Haiming Huang</author><author>Chengrui Wu</author><author>Zhanwu Zhu</author><author>Zedong He</author><author>Guoying Liu</author>
        <description><![CDATA[The structural, mechanical, electronic, magnetic, and half-metallic properties of quaternary Heusler compound CoCrScSn are studied using the GGA and GGA + U method based on first-principles calculations. It is found that Type-I structure of CoCrScSn compound is the most stable, and its ground state is ferromagnetic. At the equilibrium lattice constant, the electronic structures obtained by GGA and GGA + U methods indicate that CoCrScSn compound have typical half-metal character. The results of elastic constants and half-metallic robustness show that the mechanical stability and half-metallicity of CoCrScSn can be well-maintained in the range of 6.2–6.9 Å under GGA and 5.7–6.4 Å under GGA + U, respectively. When CoCrScSn compound exhibits half-metallic properties, the total magnetic moment per molecular unit is 4.0 μB, which is in good agreement with the Slater-Pauling rule, and Cr atoms are the main source of molecular magnetic moment. All the aforementioned results indicate that quaternary Heusler compound CoCrScSn would be an ideal candidate in spintronics.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00196</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00196</link>
        <title><![CDATA[Predicting Sites of Epitranscriptome Modifications Using Unsupervised Representation Learning Based on Generative Adversarial Networks]]></title>
        <pubdate>2020-06-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Sirajul Salekin</author><author>Milad Mostavi</author><author>Yu-Chiao Chiu</author><author>Yidong Chen</author><author>Jianqiu Zhang</author><author>Yufei Huang</author>
        <description><![CDATA[Epitranscriptome is an exciting area that studies different types of modifications in transcripts, and the prediction of such modification sites from the transcript sequence is of significant interest. However, the scarcity of positive sites for most modifications imposes critical challenges for training robust algorithms. To circumvent this problem, we propose MR-GAN, a generative adversarial network (GAN)-based model, which is trained in an unsupervised fashion on the entire pre-mRNA sequences to learn a low-dimensional embedding of transcriptomic sequences. MR-GAN was then applied to extract embeddings of the sequences in a training dataset we created for nine epitranscriptome modifications, namely, m6A, m1A, m1G, m2G, m5C, m5U, 2′-O-Me, pseudouridine (Ψ), and dihydrouridine (D), of which the positive samples are very limited. Prediction models were trained based on the embeddings extracted by MR-GAN. We compared the prediction performance with the one-hot encoding of the training sequences and SRAMP, a state-of-the-art m6A site prediction algorithm, and demonstrated that the learned embeddings outperform one-hot encoding by a significant margin for up to 15% improvement. Using MR-GAN, we also investigated the sequence motifs for each modification type and uncovered known motifs as well as new motifs not possible with sequences directly. The results demonstrated that transcriptome features extracted using unsupervised learning could lead to high precision for predicting multiple types of epitranscriptome modifications, even when the data size is small and extremely imbalanced.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00203</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00203</link>
        <title><![CDATA[Classification of Cancer Types Using Graph Convolutional Neural Networks]]></title>
        <pubdate>2020-06-17T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ricardo Ramirez</author><author>Yu-Chiao Chiu</author><author>Allen Hererra</author><author>Milad Mostavi</author><author>Joshua Ramirez</author><author>Yidong Chen</author><author>Yufei Huang</author><author>Yu-Fang Jin</author>
        <description><![CDATA[Background: Cancer has been a leading cause of death in the United States with significant health care costs. Accurate prediction of cancers at an early stage and understanding the genomic mechanisms that drive cancer development are vital to the improvement of treatment outcomes and survival rates, thus resulting in significant social and economic impacts. Attempts have been made to classify cancer types with machine learning techniques during the past two decades and deep learning approaches more recently.Results: In this paper, we established four models with graph convolutional neural network (GCNN) that use unstructured gene expressions as inputs to classify different tumor and non-tumor samples into their designated 33 cancer types or as normal. Four GCNN models based on a co-expression graph, co-expression+singleton graph, protein-protein interaction (PPI) graph, and PPI+singleton graph have been designed and implemented. They were trained and tested on combined 10,340 cancer samples and 731 normal tissue samples from The Cancer Genome Atlas (TCGA) dataset. The established GCNN models achieved excellent prediction accuracies (89.9–94.7%) among 34 classes (33 cancer types and a normal group). In silico gene-perturbation experiments were performed on four models based on co-expression graph, co-expression+singleton, PPI graph, and PPI+singleton graphs. The co-expression GCNN model was further interpreted to identify a total of 428 marker genes that drive the classification of 33 cancer types and normal. The concordance of differential expressions of these markers between the represented cancer type and others are confirmed. Successful classification of cancer types and a normal group regardless of normal tissues' origin suggested that the identified markers are cancer-specific rather than tissue-specific.Conclusion: Novel GCNN models have been established to predict cancer types or normal tissue based on gene expression profiles. We demonstrated the results from the TCGA dataset that these models can produce accurate classification (above 94%), using cancer-specific markers genes. The models and the source codes are publicly available and can be readily adapted to the diagnosis of cancer and other diseases by the data-driven modeling research community.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00138</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00138</link>
        <title><![CDATA[4DFlowNet: Super-Resolution 4D Flow MRI Using Deep Learning and Computational Fluid Dynamics]]></title>
        <pubdate>2020-05-04T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Edward Ferdian</author><author>Avan Suinesiaputra</author><author>David J. Dubowitz</author><author>Debbie Zhao</author><author>Alan Wang</author><author>Brett Cowan</author><author>Alistair A. Young</author>
        <description><![CDATA[4D flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invasive examination. This enables qualitative and quantitative analysis of hemodynamic flow parameters of the heart and great vessels. An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. However, this must be balanced with increasing imaging time. The recent success of deep learning in generating super resolution images shows promise for implementation in medical images. We utilized computational fluid dynamics simulations to generate fluid flow simulations and represent them as synthetic 4D flow MRI data. We built our training dataset to mimic actual 4D flow MRI data with its corresponding noise distribution. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2. We also tested the 4DFlowNet in actual 4D flow MR images of a phantom and normal volunteer data, and demonstrated comparable results with the actual flow rate measurements giving an absolute relative error of 0.6–5.8% and 1.1–3.8% in the phantom data and normal volunteer data, respectively.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00042</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00042</link>
        <title><![CDATA[Physics-Informed Neural Networks for Cardiac Activation Mapping]]></title>
        <pubdate>2020-02-28T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Francisco Sahli Costabal</author><author>Yibo Yang</author><author>Paris Perdikaris</author><author>Daniel E. Hurtado</author><author>Ellen Kuhl</author>
        <description><![CDATA[A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior knowledge of the underlying physics nor uncertainty of these recordings. Here we propose a physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics and we quantify the epistemic uncertainty associated with these predictions. These uncertainty estimates not only allow us to quantify the predictive error of the neural network, but also help to reduce it by judiciously selecting new informative measurement locations via active learning. We illustrate the potential of our approach using a synthetic benchmark problem and a personalized electrophysiology model of the left atrium. We show that our new method outperforms linear interpolation and Gaussian process regression for the benchmark problem and linear interpolation at clinical densities for the left atrium. In both cases, the active learning algorithm achieves lower error levels than random allocation. Our findings open the door toward physics-based electro-anatomic mapping with the ultimate goals to reduce procedural time and improve diagnostic predictability for patients affected by atrial fibrillation. Open source code is available at https://github.com/fsahli/EikonalNet.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2020.00030</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2020.00030</link>
        <title><![CDATA[Deep Learning Over Reduced Intrinsic Domains for Efficient Mechanics of the Left Ventricle]]></title>
        <pubdate>2020-02-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Gonzalo D. Maso Talou</author><author>Thiranja P. Babarenda Gamage</author><author>Mark Sagar</author><author>Martyn P. Nash</author>
        <description><![CDATA[Cardiac mechanics tools can be used to enhance medical diagnosis and treatment, and assessment of risk of cardiovascular diseases. Still, the computational cost to solve cardiac models restricts their use for online applications and routine clinical practice. This work presents a surrogate model obtained by training a set of Siamese networks over a physiological representation of the left ventricle. Our model allows us to modify the geometry, loading conditions, and material properties without needing of retraining. Additionally, we propose the novel concept of intrinsic domain that improves the accuracy of the network predictions by one order of magnitude. The neural networks were trained and tested with numerical predictions from a previously published finite element model of the left ventricle. Different loading conditions, material properties and geometrical definitions of the domain were simulated by the model leading to a dataset of 5, 670 cases. In terms of accuracy and performance, the surrogate model approximates the displacement field of the finite element model with an error of 4.4 ± 2.9% (with respect to the L2-norm of the true displacement field) across all cases while performing computations 62 times faster. Hence, the trained model is capable of computing a passive cardiac filling of the chamber at 10 different time points in just ~0.7 s. These outcomes prove usability of training surrogate models for efficient simulations to facilitate the use of complex mechanical models in clinical practice for therapeutic planning and online diagnosis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2019.00247</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2019.00247</link>
        <title><![CDATA[Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations]]></title>
        <pubdate>2020-01-21T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Ritvik Vasan</author><author>Meagan P. Rowan</author><author>Christopher T. Lee</author><author>Gregory R. Johnson</author><author>Padmini Rangamani</author><author>Michael Holst</author>
        <description><![CDATA[In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2019.00235</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2019.00235</link>
        <title><![CDATA[A Deep Learning Approach to Predict Abdominal Aortic Aneurysm Expansion Using Longitudinal Data]]></title>
        <pubdate>2020-01-15T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Zhenxiang Jiang</author><author>Huan N. Do</author><author>Jongeun Choi</author><author>Whal Lee</author><author>Seungik Baek</author>
        <description><![CDATA[An abdominal aortic aneurysm (AAA) is a gradual enlargement of the aorta that can cause a life-threatening event when a rupture occurs. Aneurysmal geometry has been proved to be a critical factor in determining when to surgically treat AAAs, but, it is challenging to predict the patient-specific evolution of an AAA with biomechanical or statistical models. The recent success of deep learning in biomedical engineering shows promise for predictive medicine. However, a deep learning model requires a large dataset, which limits its application to the prediction of the patient-specific AAA expansion. In order to cope with the limited medical follow-up dataset of AAAs, a novel technique combining a physical computational model with a deep learning model is introduced to predict the evolution of AAAs. First, a vascular Growth and Remodeling (G&R) computational model, which is able to capture the variations of actual patient AAA geometries, is employed to generate a limited in silico dataset. Second, the Probabilistic Collocation Method (PCM) is employed to reproduce a large in silico dataset by approximating the G&R simulation outputs. A Deep Belief Network (DBN) is then trained to provide fast predictions of patient-specific AAA expansion, using both in silico data and patients' follow-up data. Follow-up Computer Tomography (CT) scan images from 20 patients are employed to demonstrate the effectiveness and the feasibility of the proposed model. The test results show that the DBN is able to predict the enlargements of AAAs with an average relative error of 3.1%, which outperforms the classical mixed-effect model by 65%.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2019.00203</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2019.00203</link>
        <title><![CDATA[First-Principles Calculation of Physical Tensors of α-Diisopropylammonium Bromide (α-DIPAB) Molecular Ferroelectric Crystal]]></title>
        <pubdate>2019-11-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ahmad Alsaad</author><author>Nabil Al-Aqtash</author><author>Renat F. Sabirianov</author><author>Ahmad Ahmad</author><author>Qais M. Al-Bataineh</author><author>Issam Qattan</author><author>Zaid Albataineh</author>
        <description><![CDATA[We report accurate calculations of tonsorial elements of α-Diisopropylammonium bromide (α-DIPAB) molecular ferroelectric crystal. In particular, elastic, piezoelectric and dielectric tensors were computed using density functional theory (DFT)-based Vienna ab initio simulation package (VASP). The determination of above parameters allows an accurate description of the energy landscape for modeling of realistic devices at finite temperatures. We determine the major physical tensors in energy expansion of total energy per volume of un-deformed crystal to provide experimentalists with valuable information for designing and fabrication of pyroelectric detectors, capacitors, piezoelectric devices based on α-DIPAB. The spontaneous polarization Ps was calculated using Berry phase approach and found to be 22.64 μC/cm2 in agreement with reported theoretical value. Furthermore, we calculate dynamical Born effective charge tensor to get a deeper insight into the bonding network and lattice dynamic of α-DIPAB crystal. The neighboring layers of DIPA molecules were found to be strongly crenelated due to the strong short-ranged electrostatic repulsion between Br sites in the DIPAB crystal structure. The organization of species in DIPA molecular layer as well as in the bromine “stitching” layer is essential for accurate calculation of DIPAB elastic properties. Having understood the actual network bonding in α-DIPAB, we calculated the components of the elastic moduli tensor. Our results indicate that a Young's modulus of 50–150 GPa and a shear modulus of 4–26 GPa were found. Thus, α-DIPAB phase has a great potential to be a terrific candidate for flexible electronic device applications. The value of the principle component of electronic contribution to the static dielectric tensor of α-DIPAB is found to be ≈2.5, i.e., 50% smaller than that in typical perovskite-based ferroelectrics. Therefore, α-DIPAB is anticipated to exhibit creative materials' innovations. It could be potential candidate as insulating layer of polymer thick films. Its mechanical, insulating and elastic properties make it eligible for switch keys and flex-circuit applications. Furthermore, clamped-ion piezoelectric tensor is calculated. Our results indicate a reasonable piezoelectric response of this polar crystal making it a low cost attractive candidate for piezoelectric applications.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2019.00187</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2019.00187</link>
        <title><![CDATA[Dynamics of the Fluctuating Flying Chain]]></title>
        <pubdate>2019-11-19T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Eirik G. Flekkøy</author><author>Marcel Moura</author><author>Knut Jørgen Måløy</author>
        <description><![CDATA[A chain which is made to flow from a container, forms a striking arch that rises well above the container top. This phenomenon is caused by the well known Mould effect and is explained by a supply of momentum from the container, causing an upwards kick. Here we introduce a theory that allows for dynamic fluctuations of the chain and compare with corresponding simulations and experiments. The predictions for the chain velocity and fountain height agree well with experiments. We also explore the underlying mechanism for this momentum transfer for different chain models and find that it depends subtly on the nature of the chain as well as on the container.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2019.00154</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2019.00154</link>
        <title><![CDATA[Fluocell for Ratiometric and High-Throughput Live-Cell Image Visualization and Quantitation]]></title>
        <pubdate>2019-10-23T00:00:00Z</pubdate>
        <category>Methods</category>
        <author>Qin Qin</author><author>Shannon Laub</author><author>Yiwen Shi</author><author>Mingxing Ouyang</author><author>Qin Peng</author><author>Jin Zhang</author><author>Yingxiao Wang</author><author>Shaoying Lu</author>
        <description><![CDATA[Spatiotemporal regulation of molecular activities dictates cellular function and fate. Investigation of dynamic molecular activities in live cells often requires the visualization and quantitation of fluorescent ratio image sequences with subcellular resolution and in high throughput. Hence, there is a great need for convenient software tools specifically designed with these capabilities. Here we describe a well-characterized open-source software package, Fluocell, customized to visualize pixelwise ratiometric images and calculate ratio time courses with subcellular resolution and in high throughput. Fluocell also provides group statistics and kinetic analysis functions for the quantified time courses, as well as 3D structure and function visualization for ratio images. The application of Fluocell is demonstrated by the ratiometric analysis of intensity images for several single-chain Förster (or fluorescence) resonance energy transfer (FRET)-based biosensors, allowing efficient quantification of dynamic molecular activities in a heterogeneous population of single live cells. Our analysis revealed distinct activation kinetics of Fyn kinase in the cytosolic and membrane compartments, and visualized a 4D spatiotemporal distribution of epigenetic signals in mitotic cells. Therefore, Fluocell provides an integrated environment for ratiometric live-cell image visualization and analysis, which generates high-quality single-cell dynamic data and allows the quantitative machine-learning of biophysical and biochemical computational models for molecular regulations in cells and tissues.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fphy.2019.00117</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fphy.2019.00117</link>
        <title><![CDATA[Prediction of Left Ventricular Mechanics Using Machine Learning]]></title>
        <pubdate>2019-09-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Yaghoub Dabiri</author><author>Alex Van der Velden</author><author>Kevin L. Sack</author><author>Jenny S. Choy</author><author>Ghassan S. Kassab</author><author>Julius M. Guccione</author>
        <description><![CDATA[The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 ± 0.584 ml, XGBoost pressure: 1.544 ± 0.298 mmHg, Cubist volume: 1.495 ± 0.260 ml, Cubist pressure: 1.623 ± 0.191 mmHg, myofiber stress: 0.334 ± 0.228 kPa, cross myofiber stress: 0.075 ± 0.024 kPa, and shear stress: 0.050 ± 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.]]></description>
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