Bibliografías temáticas / Stochastic second order methods

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Literatura académica sobre el tema "Stochastic second order methods"

Autor: Grafiati
Publicado: 1 de junio de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "Stochastic second order methods"

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Burrage, Kevin, Ian Lenane, and Grant Lythe. "Numerical Methods for Second‐Order Stochastic Differential Equations." SIAM Journal on Scientific Computing 29, no. 1 (2007): 245–64. http://dx.doi.org/10.1137/050646032.

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Tocino, A., and J. Vigo-Aguiar. "Weak Second Order Conditions for Stochastic Runge--Kutta Methods." SIAM Journal on Scientific Computing 24, no. 2 (2002): 507–23. http://dx.doi.org/10.1137/s1064827501387814.

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Moxnes, John F., and Kjell Hausken. "Introducing Randomness into First-Order and Second-Order Deterministic Differential Equations." Advances in Mathematical Physics 2010 (2010): 1–42. http://dx.doi.org/10.1155/2010/509326.

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We incorporate randomness into deterministic theories and compare analytically and numerically some well-known stochastic theories: the Liouville process, the Ornstein-Uhlenbeck process, and a process that is Gaussian and exponentially time correlated (Ornstein-Uhlenbeck noise). Different methods of achieving the marginal densities for correlated and uncorrelated noise are discussed. Analytical results are presented for a deterministic linear friction force and a stochastic force that is uncorrelated or exponentially correlated.
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Komori, Yoshio. "Weak second-order stochastic Runge–Kutta methods for non-commutative stochastic differential equations." Journal of Computational and Applied Mathematics 206, no. 1 (2007): 158–73. http://dx.doi.org/10.1016/j.cam.2006.06.006.

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Tang, Xiao, and Aiguo Xiao. "Efficient weak second-order stochastic Runge–Kutta methods for Itô stochastic differential equations." BIT Numerical Mathematics 57, no. 1 (2016): 241–60. http://dx.doi.org/10.1007/s10543-016-0618-9.

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Rößler, Andreas. "Second Order Runge–Kutta Methods for Itô Stochastic Differential Equations." SIAM Journal on Numerical Analysis 47, no. 3 (2009): 1713–38. http://dx.doi.org/10.1137/060673308.

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Rößler, Andreas. "Second order Runge–Kutta methods for Stratonovich stochastic differential equations." BIT Numerical Mathematics 47, no. 3 (2007): 657–80. http://dx.doi.org/10.1007/s10543-007-0130-3.

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Wang, Xiao, and Hongchao Zhang. "Inexact proximal stochastic second-order methods for nonconvex composite optimization." Optimization Methods and Software 35, no. 4 (2020): 808–35. http://dx.doi.org/10.1080/10556788.2020.1713128.

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Abdulle, Assyr, Gilles Vilmart, and Konstantinos C. Zygalakis. "Weak Second Order Explicit Stabilized Methods for Stiff Stochastic Differential Equations." SIAM Journal on Scientific Computing 35, no. 4 (2013): A1792—A1814. http://dx.doi.org/10.1137/12088954x.

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Komori, Yoshio, and Kevin Burrage. "Weak second order S-ROCK methods for Stratonovich stochastic differential equations." Journal of Computational and Applied Mathematics 236, no. 11 (2012): 2895–908. http://dx.doi.org/10.1016/j.cam.2012.01.033.

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Tesis sobre el tema "Stochastic second order methods"

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Yuan, Rui. "Stochastic Second Order Methods and Finite Time Analysis of Policy Gradient Methods." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT010.

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Pour résoudre les problèmes de machine learning à grande échelle, les méthodes de premier ordre telles que la descente du gradient stochastique et l'ADAM sont les méthodes de choix en raison de leur coût pas cher par itération. Le problème des méthodes du premier ordre est qu'elles peuvent nécessiter un réglage important des paramètres et/ou une connaissance des paramètres du problème. Il existe aujourd'hui un effort considérable pour développer des méthodes du second ordre stochastiques efficaces afin de résoudre des problèmes de machine learning à grande échelle. La motivation est qu'elles d
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Auffredic, Jérémy. "A second order Runge–Kutta method for the Gatheral model." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-49170.

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In this thesis, our research focus on a weak second order stochastic Runge–Kutta method applied to a system of stochastic differential equations known as the Gatheral Model. We approximate numerical solutions to this system and investigate the rate of convergence of our method. Both call and put options are priced using Monte-Carlo simulation to investigate the order of convergence. The numerical results show that our method is consistent with the theoretical order of convergence of the Monte-Carlo simulation. However, in terms of the Runge-Kutta method, we cannot accept the consistency of our
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Cheng, Jianqiang. "Stochastic Combinatorial Optimization." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112261.

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Dans cette thèse, nous étudions trois types de problèmes stochastiques : les problèmes avec contraintes probabilistes, les problèmes distributionnellement robustes et les problèmes avec recours. Les difficultés des problèmes stochastiques sont essentiellement liées aux problèmes de convexité du domaine des solutions, et du calcul de l’espérance mathématique ou des probabilités qui nécessitent le calcul complexe d’intégrales multiples. A cause de ces difficultés majeures, nous avons résolu les problèmes étudiées à l’aide d’approximations efficaces.Nous avons étudié deux types de problèmes stoch
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Sabbagh, Wissal. "Some Contributions on Probabilistic Interpretation For Nonlinear Stochastic PDEs." Thesis, Le Mans, 2014. http://www.theses.fr/2014LEMA1019/document.

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L'objectif de cette thèse est l'étude de la représentation probabiliste des différentes classes d'EDPSs non-linéaires(semi-linéaires, complètement non-linéaires, réfléchies dans un domaine) en utilisant les équations différentielles doublement stochastiques rétrogrades (EDDSRs). Cette thèse contient quatre parties différentes. Nous traitons dans la première partie les EDDSRs du second ordre (2EDDSRs). Nous montrons l'existence et l'unicité des solutions des EDDSRs en utilisant des techniques de contrôle stochastique quasi- sure. La motivation principale de cette étude est la représentation pro
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El-Sharif, Najla Saleh Ahmed. "Second-order methods for some nonlinear second-order initial-value problems with forcing." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309501.

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Noubiagain, Chomchie Fanny Larissa. "Contributions to second order reflected backward stochastic differentials equations." Thesis, Le Mans, 2017. http://www.theses.fr/2017LEMA1016/document.

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Cette thèse traite des équations différentielles stochastiques rétrogrades réfléchies du second ordre dans une filtration générale . Nous avons traité tout d'abord la réflexion à une barrière inférieure puis nous avons étendu le résultat dans le cas d'une barrière supérieure. Notre contribution consiste à démontrer l'existence et l'unicité de la solution de ces équations dans le cadre d'une filtration générale sous des hypothèses faibles. Nous remplaçons la régularité uniforme par la régularité de type Borel. Le principe de programmation dynamique pour le problème de contrôle stochastique robu
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Karlgaard, Christopher David. "Second-Order Relative Motion Equations." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/34025.

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This thesis presents an approximate solution of second order relative motion equations. The equations of motion for a Keplerian orbit in spherical coordinates are expanded in Taylor series form using reference conditions consistent with that of a circular orbit. Only terms that are linear or quadratic in state variables are kept in the expansion. A perturbation method is employed to obtain an approximate solution of the resulting nonlinear differential equations. This new solution is compared with the previously known solution of the linear case to show improvement, and with numerical integrat
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Rodríguez, Cuesta Mª José. "Limit of detection for second-order calibration methods." Doctoral thesis, Universitat Rovira i Virgili, 2006. http://hdl.handle.net/10803/9013.

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Analytical chemistry can be split into two main types, qualitative and quantitative. Most modern analytical chemistry is quantitative. Popular sensitivity to health issues is aroused by the mountains of government regulations that use science to, for instance, provide public health information to prevent disease caused by harmful exposure to toxic substances. The concept of the minimum amount of an analyte or compound that can be detected or analysed appears in many of these regulations (for example, to discard the presence of traces of toxic substances in foodstuffs) generally as a part of me
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Snyman, H. "Second order analyses methods for stirling engine design." Thesis, Stellenbosch : University of Stellenbosch, 2007. http://hdl.handle.net/10019.1/16102.

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Thesis (MScIng( Mechanical Engineering)--University of Stellenbosch, 2007.<br>121 Leaves printed single pages, preliminary pages a-l and numbered pages 1-81.<br>ENGLISH ABSTRACT:In the midst of the current non-renewable energy crises specifically with regard to fossil fuel, various research institutions across the world have turned their focus to renewable and sustainable development. Using our available non.renewable resources as efficiently as possible has been a focal point the past decades and will certainly be as long as these resources exist Various means to utilize the world's abu
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Zhou, Chao. "Model Uncertainty in Finance and Second Order Backward Stochastic Differential Equations." Palaiseau, Ecole polytechnique, 2012. https://pastel.hal.science/docs/00/77/14/37/PDF/Thesis_ZHOU_Chao_Pastel.pdfcc.

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L'objectif principal de cette thèse est d'étudier quelques problèmes de mathématiques financières dans un marché incomplet avec incertitude sur les modèles. Récemment, la théorie des équations différentielles stochastiques rétrogrades du second ordre (2EDSRs) a été développée par Soner, Touzi et Zhang sur ce sujet. Dans cette thèse, nous adoptons leur point de vue. Cette thèse contient quatre parties dans le domain des 2EDSRs. Nous commençons par généraliser la théorie des 2EDSRs initialement introduite dans le cas de générateurs lipschitziens continus à celui de générateurs à croissance quadr
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Libros sobre el tema "Stochastic second order methods"

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Lan, Guanghui. First-order and Stochastic Optimization Methods for Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39568-1.

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Shepherd, Adrian J. Second-Order Methods for Neural Networks. Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0953-2.

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Strassert, Günter. The balancing principle, strict superiority relations, and a transitive overall final order of options. Institut für Regionalwissenschaft der Universität Karlsruhe, 2000.

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Otmani, Zoulikha Zaidi ep. Numerical methods for second order parabolic partial differential equations. Brunel University, 1986.

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Aamir, Shabbir, and United States. National Aeronautics and Space Administration., eds. Methods of ensuring realizability for non-realizable second order closures. National Aeronautics and Space Administration, 1994.

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Shepherd, Adrian J. Second-order methods for neural networks: Fast and reliable training methods for multi-layer perceptrons. Springer, 1997.

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Krispin, J. Second-order Godunov methods and self-similar steady supersonic three-dimensional flowfields. American Institute of Aeronautics and Astronautics, 1991.

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Heinrich, Bernd. Finite difference methods on irregular networks: A generalized approach to second order elliptic problems. Akademie-Verlag, 1987.

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Heinrich, Bernd. Finite difference methods on irregular networks: A generalized approach to second order elliptic problems. Birkhäuser Verlag, 1987.

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International Conference on Spectral and High Order Methods (2nd 1992 Montpellier, France). ICOSAHOM'92: Selected papers from the second International Conference on Spectral and High Order Methods, Montpellier, France, 22-26 June 1992. North-Holland, 1994.

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Capítulos de libros sobre el tema "Stochastic second order methods"

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Heindl, Armin, Gábor Horváth, and Karsten Gross. "Explicit Inverse Characterizations of Acyclic MAPs of Second Order." In Formal Methods and Stochastic Models for Performance Evaluation. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11777830_8.

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Cannarsa, P., and G. Da Prato. "Second order Hamilton-Jacobi equations in infinite dimensions and stochastic optimal control problems." In Probabilistic and Stochastic Methods in Analysis, with Applications. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2791-2_30.

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Fábián, Csaba I., Gautam Mitra, Diana Roman, et al. "Portfolio Choice Models Based on Second-Order Stochastic Dominance Measures: An Overview and a Computational Study." In Stochastic Optimization Methods in Finance and Energy. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9586-5_18.

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Vatanen, Tommi, Tapani Raiko, Harri Valpola, and Yann LeCun. "Pushing Stochastic Gradient towards Second-Order Methods – Backpropagation Learning with Transformations in Nonlinearities." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42054-2_55.

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Gao, Hongchang, and Heng Huang. "Faster Stochastic Second Order Method for Large-Scale Machine Learning Models." In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976700.46.

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Resseguier, Valentin. "Maximum Likelihood Estimation of Subgrid Flows from Tracer Image Sequences." In Mathematics of Planet Earth. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70660-8_12.

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AbstractFrom a sequence of tracer satellite images, several methods (e.g. optical flow) exist to successfully estimate the main advecting current. Yet, this estimate is limited in resolution. To go beyond, we propose a new parametric estimation method to estimate second-order statistics of the residual small-scale velocity. We first express stochastic transport in a discrete setting to apply standard MLE techniques. Then we propose an efficient method to solve the MLE optimization problem through a fast log-likelihood gradient evaluation algorithm.
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Gao, Jiaxin, Yao Lyu, Wenxuan Wang, Yuming Yin, Fei Ma, and Shengbo Eben Li. "Gradient Correction for Asynchronous Stochastic Gradient Descent in Reinforcement Learning." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_127.

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AbstractDistributed stochastic gradient descent techniques have gained significant attention in recent years as a prevalent approach for reinforcement learning. Current distributed learning predominantly employs synchronous or asynchronous training strategies. While the asynchronous scheme avoids idle computing resources present in synchronous methods, it grapples with the stale gradient issue. This paper introduces a novel gradient correction algorithm aimed at alleviating the stale gradient problem. By leveraging second-order information within the worker node and incorporating current param
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Ben Arous, Gérard, and Peter Laurence. "Second Order Expansion for Implied Volatility in Two Factor Local Stochastic Volatility Models and Applications to the Dynamic $$\lambda $$ -Sabr Model." In Large Deviations and Asymptotic Methods in Finance. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11605-1_4.

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Zhang, Jianfeng. "Second Order BSDEs." In Backward Stochastic Differential Equations. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7256-2_12.

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Rozovskii, B. L. "Ito’s Second Order Parabolic Equations." In Stochastic Evolution Systems. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-011-3830-7_4.

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Actas de conferencias sobre el tema "Stochastic second order methods"

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Jiang, Zhanhong, Md Zahid Hasan, Aditya Balu, Joshua R. Waite, Genyi Huang, and Soumik Sarkar. "FUSE: First-Order and Second-Order Unified Synthesis in Stochastic Optimization." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00140.

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Yu, Dongyuan, and Xiaofeng Zong. "Delay-Tolerant for Stablity Second-Order Stochastic Systems with Multiplicative Noises." In 2025 IEEE International Conference on Industrial Technology (ICIT). IEEE, 2025. https://doi.org/10.1109/icit63637.2025.10965241.

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Gao, Hongchang, and Heng Huang. "Stochastic Second-Order Method for Large-Scale Nonconvex Sparse Learning Models." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/294.

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Sparse learning models have shown promising performance in the high dimensional machine learning applications. The main challenge of sparse learning models is how to optimize it efficiently. Most existing methods solve this problem by relaxing it as a convex problem, incurring large estimation bias. Thus, the sparse learning model with nonconvex constraint has attracted much attention due to its better performance. But it is difficult to optimize due to the non-convexity. In this paper, we propose a linearly convergent stochastic second-order method to optimize this nonconvex problem for large
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Agafonov, Artem, Pavel Dvurechensky, Gesualdo Scutari, et al. "An Accelerated Second-Order Method for Distributed Stochastic Optimization." In 2021 60th IEEE Conference on Decision and Control (CDC). IEEE, 2021. http://dx.doi.org/10.1109/cdc45484.2021.9683400.

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Canhanga, Betuel, Ying Ni, Milica Rančić, Anatoliy Malyarenko, and Sergei Silvestrov. "Numerical methods on European option second order asymptotic expansions for multiscale stochastic volatility." In ICNPAA 2016 WORLD CONGRESS: 11th International Conference on Mathematical Problems in Engineering, Aerospace and Sciences. Author(s), 2017. http://dx.doi.org/10.1063/1.4972627.

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Naess, A., and H. C. Karlsen. "Nonlinear, Second-Order Response Statistics of Compliant Offshore Structures." In ASME 2003 22nd International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2003. http://dx.doi.org/10.1115/omae2003-37127.

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The paper continues previous work to develop a new method for calculating the mean level crossing rate of a stochastic process represented as a second order stochastic Volterra series. The derivation of this procedure consists of three stages. First the expression for the mean crossing rate is rewritten in terms of a joint characteristic function. Secondly, it is noted that a closed form expression for this joint characteristic function can be derived. Thirdly, it is then demonstrated how the method of steepest descent can be applied to the numerical calculation of the mean crossing rate. It i
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Mane, Vibha, Monica F. Bugallo, and Petar M. Djuric. "Stochastic modeling of second order reactions using a moment propagation method." In 2009 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS). IEEE, 2009. http://dx.doi.org/10.1109/gensips.2009.5174358.

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Tang, Rui. "A New Second-order Bistable Adaptive Stochastic Resonance Noise Reduction Method." In 8th International Conference on Social Network, Communication and Education (SNCE 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/snce-18.2018.25.

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Naess, A., H. C. Karlsen, and P. S. Teigen. "Accurate Numerical Methods for Calculating the Response Statistics of Compliant Offshore Structures." In ASME 2005 24th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2005. http://dx.doi.org/10.1115/omae2005-67236.

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The state-of-the-art representation of the horizontal motions of e.g. a TLP in random seas is in terms of a second order stochastic Volterra series. Until recently, there has been no method available for accurately calculating the mean level upcrossing rate of such response processes. Since the mean upcrossing rate is a key parameter for estimating the large and extreme responses it is clearly of importance to develop methods for its calculation. The paper describes numerical methods for calculating the mean level upcrossing rate of a stochastic response process represented as a second order s
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Jensen, Jo̸rgen Juncher. "Extreme Response Predictions for Jack-Up Units in Second Order Stochastic Waves by FORM." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29022.

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The aim of the present paper is to advocate for a very effective stochastic procedure, based on the First Order Reliability Method (FORM), for extreme value predictions related to wave induced loads. All kinds of non-linearities can be included, as the procedure makes use of short time-domain simulations of the response in question. The procedure will be illustrated with a jack-up rig where second order stochastic waves are included in the analysis. The result is the probability of overturning as function of sea state and operational time.
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Informes sobre el tema "Stochastic second order methods"

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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Petersson, N., and B. Sjogreen. Serpentine: Finite Difference Methods for Wave Propagation in Second Order Formulation. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1046802.

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Mitchell, Jason W. Implementing Families of Implicit Chebyshev Methods with Exact Coefficients for the Numerical Integration of First- and Second-Order Differential Equations. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada404958.

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Rutan, S. C. Enhancement of fluorescence detection in chromatographic methods by computer analysis of second order data. Progress report, August 1, 1990--October 1, 1993. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10163516.

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Nobile, F., Q. Ayoul-Guilmard, S. Ganesh, et al. D6.5 Report on stochastic optimisation for wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.04.

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This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind engineering. The measure of risk throughout the report is the conditional value at risk. First, the reference method is presented: the derivation of sensitivities of the risk measure; their accurate computation; and lastly, a practical optimisation algorithm with adaptive statistical estimation. Second, this method is directly applied to a nonlinear relaxation oscillator (FitzHugh–Nagumo model) with numerical experiments t
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Соловйов, Володимир Миколайович, and D. N. Chabanenko. Financial crisis phenomena: analysis, simulation and prediction. Econophysic’s approach. Гумбольдт-Клуб Україна, 2009. http://dx.doi.org/10.31812/0564/1138.

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With the beginning of the global financial crisis, which attracts the attention of the international community, the inability of existing methods to predict the events became obvious. Creation, testing, adaptation of the models to the concrete financial market segments for the purpose of monitoring, early prediction, prevention and notification of financial crises is gaining currency nowadays. Econophysics is an interdisciplinary research field, applying theories and methods originally developed by physicists in order to solve problems in economics, usually those including uncertainty or stoch
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Escobar Hernández, José Carlos. Working paper PUEAA No. 15. Teaching Spanish to Japanese students: The students’ profile, their needs and their learning style. Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.013r.2022.

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This paper focuses on the Japanese students’ learning process when they study Spanish as a second language. First, it mentions some students’ profile characteristic and their interests in learning a new language. Second, it describes the learning language system in Japan, the students’ behavior in the language classes, and which activities they prefer to do in class. In addition, it describes different kinds of learning methods that could be applied depending on the students’ interests and cultural differences. Finally, the author considers that teaching Spanish to Japanese students raises sev
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Yang, Yu, Hen-Geul Yeh, and Bryan Aguirre. Fuel Cell System Development for Heavy Duty Vehicles. Mineta Transportation Institute, 2025. https://doi.org/10.31979/mti.2025.2441.

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As California advances its ambitious goals for transportation electrification to combat climate change, hydrogen-powered fuel cells are emerging as a viable solution for overcoming the challenges of heavy-duty vehicles, offering an efficient alternative to lithium-ion batteries because they produce minimal chemical, thermal, and carbon emissions. One type of hydrogen fuel cell technology called proton exchange membrane fuel cells (PEMFCs) has garnered the most attention due to its distinct advantages, including relatively low operating temperatures (60–80 °C) and reliable performance at high c
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Gupte, Jaideep, Louise Clark, Debjani Ghosh, et al. Embedding Community Voice into Smart City Spatial Planning. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/ids.2022.005.

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Public participation in spatial planning is a vital means to successful policymaking and can be enhanced by combining geospatial methods with participatory learning and action. Based on a pilot study in Bhopal, India involving urban authorities, civil society organisations and experts in an informal settlement during Covid-19 lockdowns, we find that the obstacles to sustaining public participation are not technological, but arise from a lack of awareness of the added value of ‘second order solutions’. We outline key approaches that emphasise short-term, feasible, and low-cost ways to embed com
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Oliver, Sandy, Dayana Minchenko, Mukdarut Bangpan, Kelly Dickson, Claire Stansfield, and Janice Tripney. Evidence claims for informing decisions relating to socio-economic development. Centre for Excellence and Development Impact and Learning (CEDIL), 2023. http://dx.doi.org/10.51744/llp2.

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The Centre of Excellence for Development Impact and Learning (CEDIL) develops and tests innovative methods for evaluation and evidence synthesis. Claims made in CEDIL studies are intended to inform socio-economic development in low- and middle-income countries (LMICs), or research about LMICs. This paper provides an overview of how, in CEDIL-funded studies, claims arising from research (termed ‘evidence claims’ for brevity) have been justified and communicated in order to inform policy decisions relating to socio-economic development. This study addresses two important questions about research
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