arXiv subjects

Yong Yuan

Publications and source records attributed to Yong Yuan.

31 records · Page 2Linked to original sources

arXiv2023

A multi-fidelity deep operator network (DeepONet) for fusing simulation and monitoring data: Application to real-time settlement prediction during tunnel construction

Ground settlement prediction during the process of mechanized tunneling is of paramount importance and remains a challenging research topic. Typically, two paradigms are existing: a physics-driven approach utilizing process-oriented computational simulation models for the tunnel-soil interaction and the settlement prediction, and a data-driven approach employing machine learning techniques to establish mappings between influencing factors and the ground settlement. To integrate the advantages of both approaches and to assimilate the data from different sources, we propose a multi-fidelity deep operator network (DeepONet) framework, leveraging the recently developed operator learning methods. The presented framework comprises of two components: a low-fidelity subnet that captures the fundamental ground settlement patterns obtained from finite element simulations, and a high-fidelity subnet that learns the nonlinear correlation between numerical models and real engineering monitoring data. A pre-processing strategy for causality is adopted to consider the spatio-temporal characteristics of the settlement during tunnel excavation. Transfer learning is utilized to reduce the training cost for the low-fidelity subnet. The results show that the proposed method can effectively capture the physical information provided by the numerical simulations and accurately fit measured data as well. Remarkably, even with very limited noisy monitoring data, the proposed model can achieve rapid, accurate, and robust predictions of the full-field ground settlement in real-time during mechanized tunnel excavation.

cs.CE↗

arXiv2023

What constraints can one pose on the maximum mass of neutron stars from multi-messenger observations?

The maximum mass of neutron stars ($M_{\rm TOV}$) plays a crucial role in understanding their equation of state (EoS). Previous studies have used the measurements for the compactness of massive pulsars and the tidal deformability of neutron stars in binary neutron star (BNS) mergers to constrain the EoS and thus the $M_{\rm TOV}$. The discovery of the most massive pulsar, PSR J0952-0607, with a mass $\sim 2.35M_{\odot}$, has provided a valuable lower limit for $M_{\rm TOV}$. Another efficient method to constrain $M_{\rm TOV}$ is by examining the type of central remnant formed after a BNS merger. Gravitational wave (GW) data can provide the total mass of the system, while accompanying electromagnetic signals can help infer the remnant type. In this study, we combine all the previous constraints and utilize the observational facts that about $24\%$ of the short gamma-ray bursts are followed by an X-ray internal plateau, which indicate that roughly this fraction of BNS mergers yield supermassive neutron stars, to perform (Markov Chain) Monte Carlo simulations. These simulations allow us to explore the probability density distribution of $M_{\rm TOV}$ and other parameters related to BNS mergers. Our findings suggest that $M_{\rm TOV}$ is likely around $2.49M_{\odot} - 2.52M_{\odot}$, with an uncertainty range of approximately [$-0.16M_{\odot}$, $0.15M_{\odot}$] ([$-0.28M_{\odot}$, $0.26M_{\odot}$]) at $1σ$ ($2σ$) confidence level. Furthermore, we examine the type of merger remnants in specific events like GW170817 and GW190425 to further constrain $M_{\rm TOV}$ and other relevant parameters, which can help to understand the physical processes involved in BNS mergers.

astro-ph.HE↗

arXiv2023

Query-dominant User Interest Network for Large-Scale Search Ranking

Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bottleneck, which do not sufficiently exploit the crucial long-term interest. In fact, there is no doubt that user long-term interest is various but noisy for instant search, and how to exploit it well still remains an open problem. To tackle this problem, in this work, we propose a novel model named Query-dominant user Interest Network (QIN), including two cascade units to filter the raw user behaviors and reweigh the behavior subsequences. Specifically, we propose a relevance search unit (RSU), which aims to search a subsequence relevant to the query first and then search the sub-subsequences relevant to the target item. These items are then fed into an attention unit called Fused Attention Unit (FAU). It should be able to calculate attention scores from the ID field and attribute field separately, and then adaptively fuse the item embedding and content embedding based on the user engagement of past period. Extensive experiments and ablation studies on real-world datasets demonstrate the superiority of our model over state-of-the-art methods. The QIN now has been successfully deployed on Kuaishou search, an online video search platform, and obtained 7.6% improvement on CTR.

cs.IR↗

arXiv2023

Constraining the ellipticity and frequency of binary neutron star remnant via its gravitational-wave and electromagnetic radiations

The nature of the merger remnant of binary neutron star (BNS) remains an open question. From the theoretical point of view, one possible outcome is a supra-massive neutron star (SMNS), which is supported by rigid rotation and through its survival of hundreds of seconds before collapsing into a black hole (BH). If this is the case, the SMNS can emit continuous gravitational waves (GW) and electromagnetic (EM) radiation, particularly in the X-ray band. In this work, the ellipticity and initial frequency of SMNS are constrained with a Bayesian framework using simulated X-ray and GW signals, which could be detected by The Transient High Energy Sky and Early Universe Surveyor (THESEUS) and Einstein Telescope (ET), respectively. We found that only considering the X-ray emission can not completely constrain the initial frequency and ellipticity of the SMNS, but it can reduce the ranges of the parameters. Afterwards, we can use the posterior distribution of the X-ray parameter estimates as a prior for the GW parameter estimates. It was found that the 95$\%$ credible region of the joint X-ray-GW analysis was about $10^5$ times smaller than that of the X-ray analysis alone.

astro-ph.HE↗

arXiv2023

Comparing hierarchical black hole mergers in star clusters and active galactic nuclei

Star clusters (SCs) and active galactic nuclei (AGNs) are promising sites for the occurrence of hierarchical black hole (BH) mergers. We use simple models to compare hierarchical BH mergers in two of the dynamical formation channels. We find that the primary mass distribution of hierarchical mergers in AGNs is higher than that in SCs, with the peaks of $\sim$$50\,M_{\odot}$ and $\sim$$13\,M_{\odot}$, respectively. The effective spin ($χ_{\rm eff}$) distribution of hierarchical mergers in SCs is symmetrical around zero as expected and $\sim$$50\%$ of the mergers have $|χ_{\rm eff}|>0.2$. The distribution of $χ_{\rm eff}$ in AGNs is narrow and prefers positive values with the peak of $χ_{\rm eff}\ge0.3$ due to the assistance of AGN disks. BH hierarchical growth efficiency in AGNs, with at least $\sim$$30\%$ of mergers being hierarchies, is much higher than the efficiency in SCs. Furthermore, there are obvious differences in the mass ratios and effective precession parameters of hierarchical mergers in SCs and AGNs. We argue that the majority of the hierarchical merger candidates detected by LIGO-Virgo may originate from the AGN channel as long as AGNs get half of the hierarchical merger rate.

astro-ph.HE↗

arXiv2023

Transfer learning based physics-informed neural networks for solving inverse problems in engineering structures under different loading scenarios

Recently, a class of machine learning methods called physics-informed neural networks (PINNs) has been proposed and gained prevalence in solving various scientific computing problems. This approach enables the solution of partial differential equations (PDEs) via embedding physical laws into the loss function. Many inverse problems can be tackled by simply combining the data from real life scenarios with existing PINN algorithms. In this paper, we present a multi-task learning method using uncertainty weighting to improve the training efficiency and accuracy of PINNs for inverse problems in linear elasticity and hyperelasticity. Furthermore, we demonstrate an application of PINNs to a practical inverse problem in structural analysis: prediction of external loads of diverse engineering structures based on limited displacement monitoring points. To this end, we first determine a simplified loading scenario at the offline stage. By setting unknown boundary conditions as learnable parameters, PINNs can predict the external loads with the support of measured data. When it comes to the online stage in real engineering projects, transfer learning is employed to fine-tune the pre-trained model from offline stage. Our results show that, even with noisy gappy data, satisfactory results can still be obtained from the PINN model due to the dual regularization of physics laws and prior knowledge, which exhibits better robustness compared to traditional analysis methods. Our approach is capable of bridging the gap between various structures with geometric scaling and under different loading scenarios, and the convergence of training is also greatly accelerated through not only the layer freezing but also the multi-task weight inheritance from pre-trained models, thus making it possible to be applied as surrogate models in actual engineering projects.

cs.CE↗

arXiv2022

GRB 211227A as a peculiar long gamma-ray burst from compact star merger

Long-duration gamma-ray bursts (GRBs) associated with supernovae (SNe) are believed to originate from massive star core-collapse events, whereas short-duration GRBs that are related to compact star mergers are expected to be accompanied by kilonovae. GRB 211227A, which lasted about 84 s, had an initial short/hard spike followed by a series of soft gamma-ray extended emission at redshift $z=$0.228. We performed follow-up observations of the optical emission using BOOTES, LCOGT, and the Lijiang 2.4m telescope, but we detected no associated supernova signature, even down to very stringent limits at such a low redshift. We observed the host galaxy within a large error-circle and roughly estimate the physical offset of GRB 211227A as $20.47\pm14.47$ kpc from the galaxy center. These properties are similar to those of GRB 060614, and suggest that the progenitor of GRB 211227A is not favored to be associated with the death of massive stars. Hence, we propose that GRB 211227A originates from a compact star merger. Calculating pseudo-kilonova emission for this case by adopting the typical parameters, we find that any associated pseudo-kilonova is too faint to be detected. If this is the case, it explains naturally the characteristics of the prompt emission, the lack of SN and kilonova emission, and the large physical offset from the galaxy center.

astro-ph.HE↗

arXiv2021

Adaptive Simulated Annealing with Greedy Search for the Circle Bin Packing Problem

We introduce a new bin packing problem, termed the circle bin packing problem with circular items (CBPP-CI). The problem involves packing all the circular items into multiple identical circle bins as compact as possible with the objective of minimizing the number of used bins. We first define the tangent occupying action (TOA) and propose a constructive greedy algorithm that sequentially packs the items into places tangent to the packed items or the bin boundaries. Moreover, to avoid falling into a local minimum trap and efficiently judge whether an optimal solution has been established, we continue to present the adaptive simulated annealing with greedy search (ASA-GS) algorithm that explores and exploits the search space efficiently. Specifically, we offer two novel local perturbation strategies to jump out of the local optimum and incorporate the greedy search to achieve faster convergence. The parameters of ASA-GS are adaptive according to the number of items so that they can be size-agnostic across the problem scale. We design two sets of new benchmark instances, and the empirical results show that ASA-GS completely outperforms the constructive greedy algorithm. Moreover, the packing density of ASA-GS on the top few dense bins is much higher than that of the state-of-the-art algorithm for the single circle packing problem, inferring the high quality of the packing solutions for CBPP-CI.

cs.CG↗

arXiv2021

Detectability of "Merger-nova" emission from a long-lived magnetar in short gamma-ray bursts

One possible progenitor of short gamma-ray bursts (GRBs) is thought to be from a double neutron star (NS) merger, and the remnant of such a merger may be a supramassive NS, which is supported by rigid rotation and through its survival of hundreds of seconds before collapsing into a black hole (BH). If this is the case, an optical/infrared transient (namely merger-nova) is generated from the ejected materials and it is powered by radioactive decay from $r$-process, spin-down energy from a supramassive NS, as well as the magnetic wind from a newborn BH. In this paper, we systematically search for the signature of a supramassive NS central engine by analyzing the X-ray emission of short GRBs with internal plateau observed by {\em Swift}, and we find that five candidates of short GRBs have such feature with redshift measurement. Then, we calculate the possible merger-nova emission from those candidates for given the typical model parameters by considering the above three energy sources, and compare its brightness with the sensitivity of some optical telescopes. We find that the merger-nova emission of GRB 060801 in K-, r-, and U-bands with variations of $M_{\rm ej}$ ($10^{-4}-10^{-2} M_{\odot}$), $κ$ ($0.1-10 ~\rm cm^{2}~g^{-1}$), and $β$ ($0.1-0.3$) is very difficult to detect using the Vera C. Rubin, Panoramic Survey Telescope and Rapid Response System (Pan-STARRS), the Zwicky Transient Facility, and the Roman Space Telescope (Roman), except for the case of large ejecta mass $M_{\rm ej}=10^{-2} M_{\odot}$. However, we are very hopeful to detect the merger-nova emission of GRBs 090515, 100625A, and 101219A using more sensitive instruments, such as Vera C. Rubin, Pan-STARRS, and Roman. Moreover, the merger-nova emission of GRB 160821B is bright enough to detect in our calculations, and it is also consistent with current real observations of merger-nova emission.

astro-ph.HE↗

arXiv2020

The electromagnetic and gravitational-wave radiations of X-ray transient CDF-S XT2

Binary neutron star (NS) mergers may result in remnants of supra-massive or even stable NS, which have been supported indirectly by observed X-ray plateau of some gamma-ray bursts (GRBs) afterglow. Recently, Xue et al. (2019) discovered a X-ray transient CDF-S XT2 that is powered by a magnetar from merger of double NS via X-ray plateau and following stepper phase. However, the decay slope after the plateau emission is a little bit larger than the theoretical value of spin-down in electromagnetic (EM) dominated by losing its rotation energy. In this paper, we assume that the feature of X-ray emission is caused by a supra-massive magnetar central engine for surviving thousands of seconds to collapse black hole. Within this scenario, we present the comparisons of the X-ray plateau luminosity, break time, and the parameters of magnetar between CDF-S XT2 and other short GRBs with internal plateau samples. By adopting the collapse time to constrain the equation of state (EOS), we find that three EOSs (GM1, DD2, and DDME2) are consistent with the observational data. On the other hand, if the most released rotation energy of magnetar is dominated by GW radiation, we also constrain the upper limit of ellipticity of NS for given EOS, and it is range in $[0.32-1.3]\times 10^{-3}$. Its GW signal can not be detected by aLIGO or even for more sensitive Einstein Telescope in the future.

astro-ph.HE↗

arXiv2020

Evidence for gravitational-wave dominated emission in the central engine of short GRB 200219A

GRB 200219A is a short gamma-ray burst (GRB) with an extended emission (EE) lasting $\sim 90$s. By analyzing data observed with the {\em Swift}/BAT and {\em Fermi}/GBM, we find that a cutoff power-law model can adequately fit the spectra of the initial short pulse with $\rm E_{p}=1387^{+232}_{-134}$ keV. More interestingly, together with the EE component and early X-ray data, it exhibits plateau emission smoothly connected with a $\sim t^{-1}$ segment and followed by an extremely steep decay. The short GRB composed of those three segments is unique in the {\em Swift} era and is very difficult to explain with the standard internal/external shock model of a black hole central engine, but could be consistent with the prediction of a magnetar central engine from the merger of an NS binary. We suggest that the plateau emission followed by a $\sim t^{-1}$ decay phase is powered by the spin-down of a millisecond magnetar, which loses its rotation energy via GW quadrupole radiation. Then, the abrupt drop decay is caused by the magnetar collapsing into a black hole before switching to EM-dominated emission. This is the first short GRB for which the X-ray emission has such an intriguing feature powered by a magnetar via GW-dominated radiation. If this is the case, one can estimate the physical parameters of a magnetar, the GW signal powered by a magnetar and the merger-nova emission are also discussed.

astro-ph.HE↗

arXiv2020

Direct dissipation-based arc-length approach for the cracking elements method

Dissipated energy, representing a monotonically increasing state variable in nonlinear fracture mechanics, can be used as a restraint for tracing the dissipation instead of the elastic unloading path of the structure response. In this work, in contrast to other energy-based approaches that use internal energy and the work done by the external loads, a novel arc-length approach is proposed. It directly extracts the dissipated energy based on crack openings and tractions (displacement jumps and cohesive forces between two surfaces of one crack), taking advantage of the global/extended method of cracking elements. Its linearized form is developed, and the stiffness factor of the arc-length restraint is naturally obtained by means of the Sherman-Morrison formula. Once cohesive cracks appear, the proposed approach can be applied until most of the fracture energy is dissipated. Results from several numerical tests, in which arc-length control and self-propagating cracks are jointly used, are presented. They demonstrate the robustness of the proposed method, which captures both global and local peak loads and all snap-back parts of the force-displacement responses of loaded structures with multiple cracks.

cs.CE↗

arXiv2020

Adaptive Large Neighborhood Search for Circle Bin Packing Problem

We address a new variant of packing problem called the circle bin packing problem (CBPP), which is to find a dense packing of circle items to multiple square bins so as to minimize the number of used bins. To this end, we propose an adaptive large neighborhood search (ALNS) algorithm, which uses our Greedy Algorithm with Corner Occupying Action (GACOA) to construct an initial layout. The greedy solution is usually in a local optimum trap, and ALNS enables multiple neighborhood search that depends on the stochastic annealing schedule to avoid getting stuck in local minimum traps. Specifically, ALNS perturbs the current layout to jump out of a local optimum by iteratively reassigns some circles and accepts the new layout with some probability during the search. The acceptance probability is adjusted adaptively using simulated annealing that fine-tunes the search direction in order to reach the global optimum. We benchmark computational results against GACOA in heterogeneous instances. ALNS always outperforms GACOA in improving the objective function, and in several cases, there is a significant reduction on the number of bins used in the packing.

cs.AI↗