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Energy Geoscience 2024年第2期5卷 225-231页

作者： Goutami Das Saumen Maiti machine learning and Geocomputing laboratory Department of Applied GeophysicsIIT(ISM)Dhanbad826004JharkhandIndia

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field *** prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore ***,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex *** overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical *** empirical method for PP prediction is comprised of data pre-processing and model establishment ***'s method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition ***-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework *** normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone *** statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model *** dataset is prepared and divided into two parts:Training and *** PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression *** Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

关键词： Well log Pore pressure machine learning(ML) IODP Hikurangi Tuaheni Zone IODP Expedition 372

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Understanding adversarial attacks on observations in deep reinforcement learning

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Science China(Information Sciences) 2024年第5期67卷 69-83页

作者： You QIAOBEN Chengyang YING Xinning ZHOU Hang SU Jun ZHU Bo ZHANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua-Bosch Joint Center for Machine Learning Institute for Artificial Intelligence Tsinghua University Peng Cheng laboratory

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease the cumulative expected reward of a victim by manipulating its observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not achieve the lowest cumulative reward since they do not generally explore the environmental ***, a framework is provided to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. The reformulation approach adopted herein generates an optimal adversary in the function space of targeted attacks, repelling them via a generic two-stage framework. In the first stage, a deceptive policy is trained by hacking the environment and discovering a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, it is theoretically shown that our adversary is strong under an appropriate noise level. Extensive experiments demonstrate the superiority of the proposed method in terms of efficiency and effectiveness, achieving state-of-the-art performance in both Atari and MuJoCo environments.

关键词： deep learning reinforcement learning adversarial robustness adversarial attack

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Enhanced Natural Convection for Accelerating Melting of Phase Change Material in Cellular Structure through Inserting Fin

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Journal of Thermal Science 2024年第2期33卷 548-563页

作者： KONG Dekui ZHANG Yongcun LIU Shutian State Key laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian116024China

The design of thermal conductivity enhancers(TCE) is quite critical to overcoming the disadvantage of the poor thermal conductivity of phase change materials(PCM).The main contribution of this study is firstly to discuss how to actively enhance natural convection of the melted PCM in cellular structure by the fin formed in the structure under the condition of the same metal mass,apart from simultaneously improving heat conduction,which can boost the heat transfer ***,a tailored hybrid fin-lattice structure(HFS) as TCE is designed and fabricated by additive manufacturing(AM).A two-equation numerical method is applied to study the heat transfer of the PCM,and its feasibility is validated with the experimental *** numerical results indicate that enhanced natural convection and improved heat conduction can be obtained simultaneously when a well-designed fin is embedded into a lattice *** enhanced natural convection results in the improved melting rate and the decreased wall temperature;e.g.,the complete melting time and the wall temperature are reduced by 11.6% and 19.7%,respectively,because of the fin for metal ***,the parameters of HFS including the porosity,pore density,and fin dimension have a great impact on the heat *** enhancement effect of the fin for HFS on the melting rate of the PCM increases as the thermal conductivity of the base material *** example,when the fin is introduced into the lattice structure,the complete melting time is reduced by 24.1% for metal *** summary,this study enables us to obtain a good understanding of the mechanism of the heat transfer and provides necessary experimental data for the structural design of HFS fabricated by AM.

关键词： natural convection heat transfer enhancement phase change material additive manufacturing hybrid fin-lattice structure

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Topology optimization Method for Microscale Structures Described with Integral Nonlocal Theory

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Acta Mechanica Solida Sinica 2024年第1期37卷 63-71页

作者： Jiayu Li Quhao Li Shutian Liu State Key laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian 116024China

The integration of additive manufacturing and topology optimization makes it possible to fabricate complex configurations,especially for microscale structures,which can guarantee the realization of high-performance structural ***,topology results often contain microstructures(several multicellular scales)similar to the characteristic length of local macrostructures,leading to errors in structural performance analysis based on classical ***,it is necessary to consider the size effect in topology *** this paper,we establish a novel topology optimization model utilizing the integral nonlocal theory to account for the size *** approach consists of an integral constitutive model that incorporates a kernel function,enabling the description of stress at a specific point in relation to strain in a distant *** optimization structures based on nonlocal theory are presented for some benchmark examples,and the results are compared with those based on classical medium *** material layout exhibits significant differences between the two approaches,highlighting the necessity of topology optimization based on nonlocal theory and the effectiveness of the proposed method.

关键词： Topology optimization Microstructures Integral nonlocal theory Size effect

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Taiji-TianQin-LISA network:Precisely measuring the Hubble constant using both bright and dark sirens

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Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 59-77页

作者： Shang-Jie Jin Ye-Zhu Zhang Ji-Yu Song Jing-Fei Zhang Xin Zhang Key laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key laboratory of Data Analytics and optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems optimization Northeastern UniversityShenyang 110819China

In the coming decades,the space-based gravitational-wave(GW)detectors such as Taiji,TianQin,and LISA are expected to form a network capable of detecting millihertz GWs emitted by the mergers of massive black hole binaries(MBHBs).In this work,we investigate the potential of GW standard sirens from the Taiji-TianQin-LISA network in constraining cosmological *** the optimistic scenario in which electromagnetic(EM)counterparts can be detected,we predict the number of detectable bright sirens based on three different MBHB population models,i.e.,popⅢ,Q3d,and *** results show that the TaijiTianQin-LISA network alone could achieve a constraint precision of 0.9%for the Hubble constant,meeting the standard of precision ***,the Taiji-TianQin-LISA network could effectively break the cosmological parameter degeneracies generated by the CMB data,particularly in the dynamical dark energy *** combined with the CMB data,the joint CMB+Taiji-TianQin-LISA data offerσ(w)=0.036 in the wCDM model,which is close to the latest constraint result obtained from the CMB+SN *** also consider a conservative scenario in which EM counterparts are not *** to the precise sky localizations of MBHBs by the Taiji-TianQin-LISA network,the constraint precision of the Hubble constant is expected to reach 1.2%.In conclusion,the GW standard sirens from the Taiji-TianQin-LISA network will play a critical role in helping solve the Hubble tension and shedding light on the nature of dark energy.

关键词： space-based gravitational wave detection standard sirens the Taiji-TianQin-LISA network the Hubble constant dark energy

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EPR-Net: constructing a non-equilibrium potential landscape via a variational force projection formulation

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National Science Review 2024年第7期11卷 135-147页

作者： Yue Zhao Wei Zhang Tiejun Li Center for Data Science Peking University Zuse Institute Berlin Department of Mathematics and Computer Science Freie Universit?t Berlin laboratory of Mathematics and Applied Mathematics(LMAM)and School of Mathematical Sciences Peking University Center for machine learning Research Peking University

We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state ***-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the orthogonal projection of the driving force of the underlying dynamics in a weighted inner-product ***, our loss function has an intimate connection with the steady entropy production rate(EPR),enabling simultaneous landscape construction and EPR estimation. We introduce an enhanced learning strategy for systems with small noise, and extend our framework to include dimensionality reduction and the state-dependent diffusion coefficient case in a unified fashion. Comparative evaluations on benchmark problems demonstrate the superior accuracy, effectiveness and robustness of EPR-Net compared to existing methods. We apply our approach to challenging biophysical problems, such as an eight-dimensional(8D)limit cycle and a 52D multi-stability problem, which provide accurate solutions and interesting insights on constructed landscapes. With its versatility and power, EPR-Net offers a promising solution for diverse landscape construction problems in biophysics.

关键词： high-dimensional potential landscape non-equilibrium system entropy production rate dimensionality reduction deep learning

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Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

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Chinese Physics C 2024年第4期48卷 240-251页

作者：孙天阳熊春雨金上捷王钰鑫张敬飞张鑫 Key laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key laboratory of Data Analytics and optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems optimization Northeastern UniversityShenyang 110819China

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave(GW)signals,thereby exerting a notable impact on the processing of GW *** inference of GW parameters,crucial for GW astronomy research,is particularly susceptible to such *** this study,we pioneer the utilization of a temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters,seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency ***,our findings indicate that the accuracy of this inference method is comparable to that of traditional non-glitch sampling ***,our approach exhibits a greater efficiency,boasting processing times on the order of *** conclusion,the application of a normalizing flow emerges as pivotal in handling GW signals affected by transient noises,offering a promising avenue for enhancing the field of GW astronomy research.

关键词： gravitational wave glitch non-Gaussian and transient noise normalizing flow machine learning likelihood-free parameter inference

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Stability analysis and stabilization of semi-Markov jump linear systems with unavailable sojourn-time information

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Science China(Information Sciences) 2024年第7期67卷 211-223页

作者： Xiaotai WU Yang TANG Shuai MAO Ying ZHAO School of Mathematics and Physics Anhui Polytechnic University Key laboratory of Advanced Control and optimization for Chemical Processes Ministry of EducationEast China University of Science and Technology

This study addresses the stability and stabilization problems of discrete-time semi-Markov jump linear systems(S-MJLSs) with unavailable sojourn-time information. The sojourn-time probability mass functions(S-TPMFs) of discrete-time semi-Markov chains are no longer confined to the geometric distribution, and it is difficult to obtain accurate and comprehensive information for S-TPMFs in practice. This is because S-TPMFs are usually deduced from the statistical characteristics according to the sampled-data, while adequate samples are often costly and time consuming. In this study, when the S-TPMFs for semi-Markov chains are assumed to be unavailable, the σ-error mean square stability is investigated for discrete-time S-MJLSs with some widely used assumptions; for semi-Markov chains, only the transition probability matrix of the embedded chain is used. In addition, the existence conditions of the effective controller are provided for closed-loop systems without using the information of S-TPMFs. Numerical examples are presented to illustrate the validity of the obtained theoretical results.

关键词： semi-Markov chain semi-Markov jump linear systems $\sigma$-error mean square stability unavailable sojourn-time information

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Numerical and experimental research on axial injection end-burning hybrid rocket motors with polyethylene fuel

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Chinese Journal of Aeronautics 2024年第8期37卷 91-105页

作者： Xiangyu MENG Hui TIAN Lingfei HE Jingfei GAO Xiaoting NIU Guobiao CAI School of Astronautics Beihang UniversityBeijing 100191China Key laboratory of Spacecraft Design optimization&Dynamic Simulation Technologies Ministry of EducationBeijing 100816China

This study investigates the end-burning hybrid rocket motors with polyethylene fuel by the numerical simulation and *** on computational fluid dynamics,a numerical model is *** model is validated by two firing tests in this hybrid rocket motor,which uses oxygen and polyethylene as *** results show that the numerical and experimental data are in good agreement,and the error of the chamber pressure is less than 2.63%.Based on the simulation mode,the blowoff limit of the end-burning hybrid rocket motors is *** the nozzle throat diameter and the inner diameter of grain are large,it is more difficult for the hybrid rocket motor to achieve end-burning mode,i.e.,the flame spreading is prevented in the narrow *** main reason is that when the nozzle throat and the grain port are large,chamber pressure and oxidizer flow velocity are ***,the friction velocity considering the pressure and flow velocity is *** critical friction velocity is about 4.054–4.890 m/s in the hybrid rocket *** the friction velocity exceeds the critical friction velocity,the combustion mode in hybrid rocket motors changes from the flame spreading mode to the end-burning ***,the regression rate formula is obtained by fitting,which shows that the regression rate has a good correlation with combustion chamber *** critical friction velocity and regression rate formula can provide an important reference for end-burning hybrid rocket motors.

关键词： Hybrid rocket motor End burning Blowoff limit Flame propagation Regression rate

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A comprehensive fluid-solid coupling dynamic simulation for spatiotemporal distribution of regression rate in hybrid rocket motors

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Chinese Journal of Aeronautics 2024年第9期37卷 100-112页

作者： Tianfang WEI Guobiao CAI Hui TIAN Xiangyu MENG Xianzhu JIANG Xiaoming GU School of Astronautics Beihang UniversityBeijing 100191China Key laboratory of Spacecraft Design optimization&Dynamic Simulation Technologies Ministry of Education Beijing 100191China

The spatiotemporal distribution characteristics of the regression rate are crucial aspects of the research on Hybrid Rocket Motor(HRM).This study presents a pioneering effort in achiev-ing a comprehensive numerical simulation of fluid dynamics and heat transfer in both the fluid and solid regions throughout the entire operation of an *** accomplish this,a dynamic grid tech-nique that incorporates fluid-solid coupling is *** validate the precision of the numerical simulations,a firing test is conducted,with embedded thermocouple probes being used to measure the inner temperature of the fuel *** temperature variations in the solid fuel obtained from both experiment and simulations show good *** maximum combustion temperature and average thrust obtained from the simulations are found to deviate from the experimental results by only 3.3%and 2.4%,***,it can be demonstrated that transient numerical simu-lations accurately capture the fluid-solid coupling characteristics and transient regression *** dynamic simulation results of inner flow field and solid region throughout the entire working stage reveal that the presence of vortices enhances the blending of combustion gases and improves the regression rate at both the front and rear ends of the fuel *** addition,oscillations of the regression rate obtained in the simulation can also be well corresponded with the corrugated surface observed in the ***,the zero-dimension regression rate formula and the for-mula describing the axial location dependence of the regression rate are fitted from the simulation results,with the corresponding coefficients of determination(R°)of 0.9765 and 0.9298,*** research serves as a reference for predicting the performance of HRM with gas oxygen and polyethylene,and presents a credible way for investigating the spatiotemporal distribution of the regression rate.

关键词： Hybrid Rocket Motor(HRM) Transient numerical simulation Fluid-solid couplingheat transfer Spatiotemporal distribution of regression rate Dynamic grid

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