IA Scholar Query: Finite-Time H∞ Filtering for Singular Stochastic Systems.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgTue, 27 Sep 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Quantum Computing for Machine Learning and Physics Simulation
https://scholar.archive.org/work/klelebrqfvbirla7srmlw4th2y
Quantum computing is widely thought to provide exponential speedups over classical algorithms for a variety of computational tasks. In classical computing, methods in artificial intelligence such as neural networks and adversarial learning have enabled drastic improvements in state-of-the-art performance for a variety of tasks. We consider the intersection of quantum computing with machine learning, including the quantum algorithms for deep learning on classical datasets, quantum adversarial learning for quantum states, and variational quantum machine learning for improved physics simulation. We consider a standard deep neural network architecture and show that conditions amenable to trainability by gradient descent coincide with those necessary for an efficient quantum algorithm. Considering the neural network in the infinite-width limit using the neural tangent kernel formalism, we propose a quantum algorithm to train the neural network with vanishing error as the training dataset size increases. Under a sparse approximation of the neural tangent kernel, the training time scales logarithmically with the number of training examples, providing the first known exponential quantum speedup for feedforward neural networks. Related approximations to the neural tangent kernel are discussed, with numerical studies showing successful convergence beyond the proven regime. Our work suggests the applicability of the quantum computing to additional neural network architectures and common datasets such as MNIST, as well as kernel methods beyond the neural tangent kernel. Generative adversarial networks (GANs) are one of the most widely adopted machine learning methods for data generation. We propose an entangling quantum GAN (EQ-GAN) that overcomes some limitations of previously proposed quantum GANs. EQ-GAN guarantees the convergence to a Nash equilibrium under minimax optimization of the discriminator and generator circuits by performing entangling operations between both the generator output and true quantum data. [...]Alexander Zlokapawork_klelebrqfvbirla7srmlw4th2yTue, 27 Sep 2022 00:00:00 GMTPhase Randomness in a Semiconductor Laser: the Issue of Quantum Random Number Generation
https://scholar.archive.org/work/ldomahbwbvdm5kntqmobcn3nhu
Gain-switched lasers are in demand in numerous quantum applications, particularly, in systems of quantum key distribution and in various optical quantum random number generators. The reason for this popularity is natural phase randomization between gain-switched laser pulses. The idea of such randomization has become so familiar that most authors use it without regard to the features of the laser operation mode they use. However, at high repetition rates of laser pulses or when pulses are generated at a bias current close to the threshold, the phase randomization condition may be violated. This paper describes theoretical and experimental methods for estimating the degree of phase randomization in a gain-switched laser. We consider in detail different situations of laser pulse interference and show that the interference signal remains quantum in nature even in the presence of classical phase drift in the interferometer provided that the phase diffusion in a laser is efficient enough. Moreover, we formulate the relationship between the previously introduced quantum reduction factor and the leftover hash lemma. Using this relationship, we develop a method to estimate the quantum noise contribution to the interference signal in the presence of phase correlations. Finally, we introduce a simple experimental method based on the analysis of statistical interference fringes, providing more detailed information about the probabilistic properties of laser pulse interference.Roman Shakhovoy, Marius Puplauskis, Violetta Sharoglazova, Alexander Duplinskiy, Denis Sych, Elizaveta Maksimova, Selbi Hydyrova, Alexander Tumachek, Yury Mironov, Vadim Kovalyuk, Alexey Prokhodtsov, Grigory Goltsman, Yury Kurochkinwork_ldomahbwbvdm5kntqmobcn3nhuMon, 26 Sep 2022 00:00:00 GMTA CNN-LSTM-based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming
https://scholar.archive.org/work/ea2o2r7dvbgmjhmtvfggdigrwq
In the sixth-generation (6G) cellular networks, hybrid beamforming would be a real-time optimization problem that is becoming progressively more challenging. Although numerical computation-based iterative methods such as the minimal mean square error (MMSE) and the alternative manifold-optimization (Alt-Min) can already attain near-optimal performance, their computational cost renders them unsuitable for real-time applications. However, recent studies have demonstrated that machine learning techniques like deep neural networks (DNN) can learn the mapping done by those algorithms between channel state information (CSI) and near-optimal resource allocation, and then approximate this mapping in near real-time. In light of this, we investigate various DNN architectures for beamforming challenges in the terahertz (THz) band for ultra-massive multiple-input multiple-output (UM-MIMO) and explore their contextual mathematical modeling. Specifically, we design a sophisticated 1D convolutional neural network and long short-term memory (1D CNN-LSTM) based fusion-separation scheme, which can approach the performance of the Alt-Min algorithm in terms of spectral efficiency (SE) and, at the same time, use significantly less computational effort. Simulation results indicate that the proposed system can attain almost the same level of SE as that of the numerical iterative algorithms, while incurring a substantial reduction in computational cost. Our DNN-based approach also exhibits exceptional adaptability to diverse network setups and high scalability. Although the current model only addresses the fully connected hybrid architecture, our approach can also be expanded to address a variety of other network topologies. INDEX TERMS 6G, CNN, Hybrid Beamforming, LSTM, UM-MIMORafid Umayer Murshedwork_ea2o2r7dvbgmjhmtvfggdigrwqMon, 26 Sep 2022 00:00:00 GMTComplex oscillatory patterns in a three-timescale model of a generalist predator and a specialist predator competing for a common prey
https://scholar.archive.org/work/wuywvnlxgjgtphkkoavvwzvwe4
In this paper, we develop and analyze a model that studies the interaction between a specialist predator, a generalist predator, and their common prey in a two-trophic ecosystem featuring three timescales. We assume that the prey operates on a faster timescale, while the specialist and generalist predators operate on slow and superslow timescales respectively. Treating the predation efficiency of the generalist predator as the primary varying parameter and the proportion of its diet formed by the prey species under study as the secondary parameter, we obtain a host of rich and interesting dynamics, including relaxation oscillations, mixed-mode oscillations (MMOs), subcritical elliptic bursting patterns, torus canards, and mixed-type torus canards. By grouping the timescales into two classes and using the timescale separation between classes, we apply one-fast/two-slow and two-fast/one-slow analysis techniques to gain insights about the dynamics. Using the geometric properties and flows of the singular subsystems, in combination with bifurcation analysis and numerical continuation of the full system, we classify the oscillatory dynamics and discuss the transitions from one type of dynamics to the other. The types of oscillatory patterns observed in this model are novel in population models featuring three-timescales; some of which qualitatively resemble natural cycles in small mammals and insects. Furthermore, oscillatory dynamics displaying torus canards, mixed-type torus canards, and MMOs experiencing a delayed loss of stability near one of the invariant sheets of the self-intersecting critical manifold before getting attracted to the adjacent attracting sheet of the critical manifold have not been previously reported in three-timescale models.Susmita Sadhuwork_wuywvnlxgjgtphkkoavvwzvwe4Sun, 25 Sep 2022 00:00:00 GMTVerifiability of the Data-Driven Variational Multiscale Reduced Order Model
https://scholar.archive.org/work/zqdym5tjwzbhreuywb2jaafh6u
In this paper, we focus on the mathematical foundations of reduced order model (ROM) closures. First, we extend the verifiability concept from large eddy simulation to the ROM setting. Specifically, we call a ROM closure model verifiable if a small ROM closure model error (i.e., a small difference between the true ROM closure and the modeled ROM closure) implies a small ROM error. Second, we prove that a data-driven ROM closure (i.e., the data-driven variational multiscale ROM) is verifiable. Finally, we investigate the verifiability of the data-driven variational multiscale ROM in the numerical simulation of the one-dimensional Burgers equation and a two-dimensional flow past a circular cylinder at Reynolds numbers Re=100 and Re=1000.Birgul Koc, Changhong Mou, Honghu Liu, Zhu Wang, Gianluigi Rozza, Traian Iliescuwork_zqdym5tjwzbhreuywb2jaafh6uSat, 24 Sep 2022 00:00:00 GMTUniversal behavior of highly-confined heat flow in semiconductor nanosystems: from nanomeshes to metalattices
https://scholar.archive.org/work/azronpytcbblxpzpybxg5ck7mi
Nanostructuring on length scales corresponding to phonon mean free paths provides control over heat flow in semiconductors and makes it possible to engineer their thermal properties. However, the influence of boundaries limits the validity of bulk models, while first principles calculations are too computationally expensive to model real devices. Here we use extreme ultraviolet beams to study phonon transport dynamics in a 3D nanostructured silicon metalattice with deep nanoscale feature size, and observe dramatically reduced thermal conductivity relative to bulk. To explain this behavior, we develop a predictive theory wherein thermal conduction separates into a geometric permeability component and an intrinsic viscous contribution, arising from a new and universal effect of nanoscale confinement on phonon flow. Using both experiments and atomistic simulations, we show that our theory is valid for a general set of highly-confined silicon nanosystems, from metalattices, nanomeshes, porous nanowires to nanowire networks. This new analytical theory of thermal conduction can be used to predict and engineer phonon transport in boundary-dominated nanosystems, that are of great interest for next-generation energy-efficient devices.Brendan McBennett, Albert Beardo, Emma E. Nelson, Begoña Abad, Travis D. Frazer, Amitava Adak, Yuka Esashi, Baowen Li, Henry C. Kapteyn, Margaret M. Murnane, Joshua L. Knoblochwork_azronpytcbblxpzpybxg5ck7miFri, 23 Sep 2022 00:00:00 GMTAdaptive Robust Tracking Control for Near Space Vehicles with Multi-Source Disturbances and Input–Output Constraints
https://scholar.archive.org/work/rtg4img7w5ce3neairx6vyeijq
In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme.Xiaohui Yan, Guiwei Shao, Qingyun Yang, Liang Yu, Yuwu Yao, Shengxia Tuwork_rtg4img7w5ce3neairx6vyeijqFri, 23 Sep 2022 00:00:00 GMTEavesdropping on competing condensates by the edge supercurrent in a Weyl superconductor
https://scholar.archive.org/work/o5oll7cud5anvkyt6g7ea6vncq
The intrinsic condensate in the superconducting state of MoTe2 is incompatible with supercurrent injected from Nb contacts, as evidenced by strong stochasticity observed in the current-voltage curves. In a magnetic field H, competition between the two pair fields produces hysteresis with an anomalous sign. The differential resistance exhibits a long train of periodic peaks with a history-dependent phase noise. By tailoring the contact geometry, we demonstrate that the periodic peaks represent the fluxoid quantization of the edge supercurrent. From the oscillations we infer the existence of a condensate blockade mechanism. The intrinsic condensate is capable of blocking the pairing action of the injected supercurrent depending on the history. The blockade leads to the antihysteretic curves in both the bulk and edge states as H is cycled.Stephan Kim, Shiming Lei, Leslie M. Schoop, R. J. Cava, N. P. Ongwork_o5oll7cud5anvkyt6g7ea6vncqFri, 23 Sep 2022 00:00:00 GMTExperimental Realization and Characterization of Stabilized Pair Coherent States
https://scholar.archive.org/work/yaporyyt2jeynllkifosvufsia
The pair coherent state (PCS) is a theoretical extension of the Glauber coherent state to two harmonic oscillators. It is an interesting class of non-Gaussian continuous-variable entangled state and is also at the heart of a promising quantum error correction code: the pair cat code. Here we report an experimental demonstration of the pair coherent state of microwave photons in two superconducting cavities. We implement a cross-cavity pair-photon driven dissipation process, which conserves the photon number difference between cavities and stabilizes the state to a specific complex amplitude. We further introduce a technique of quantum subspace tomography, which enables direct measurements of individual coherence elements of a high-dimensional quantum state without global tomographic reconstruction. We characterize our two-mode quantum state with up to 4 photons in each cavity using this subspace tomography together with direct measurements of the photon number difference and the joint Wigner function. We identify the spurious cross-Kerr interaction between the cavities and our dissipative reservoir mode as a prominent dephasing channel that limits the steady-state coherence in our current scheme. Our experiment provides a set of reservoir engineering and state characterization tools to study quantum optics and implement multi-mode bosonic codes in superconducting circuits.Jeffrey M. Gertler, Sean van Geldern, Shruti Shirol, Liang Jiang, Chen Wangwork_yaporyyt2jeynllkifosvufsiaFri, 23 Sep 2022 00:00:00 GMTArtificial Intelligence and Advanced Materials
https://scholar.archive.org/work/tkf566mg6zf77a7xan6anloxvu
Artificial intelligence is gaining strength and materials science can both contribute to and profit from it. In a simultaneous progress race, new materials, systems and processes can be devised and optimized thanks to machine learning techniques and such progress can be turned into in-novative computing platforms. Future materials scientists will profit from understanding how machine learning can boost the conception of advanced materials. This review covers aspects of computation from the fundamentals to directions taken and repercussions produced by compu-tation to account for the origins, procedures and applications of artificial intelligence. Machine learning and its methods are reviewed to provide basic knowledge on its implementation and its potential. The materials and systems used to implement artificial intelligence with electric charges are finding serious competition from other information carrying and processing agents. The impact these techniques are having on the inception of new advanced materials is so deep that a new paradigm is developing where implicit knowledge is being mined to conceive materi-als and systems for functions instead of finding applications to found materials. How far this trend can be carried is hard to fathom as exemplified by the power to discover unheard of mate-rials or physical laws buried in data.Cefe Lópezwork_tkf566mg6zf77a7xan6anloxvuFri, 23 Sep 2022 00:00:00 GMTFully data-driven time-delay interferometry with time-varying delays
https://scholar.archive.org/work/z2fw2r62nzhphkywj72vi3u2r4
We recently introduced the basic concepts of an approach to filtering strongly laser-noise dominated space-based gravitational-wave data, like LISA's phase comparison data streams, which does not rely on independent knowledge of a temporal delays pattern in the dominant noise that generates the data. Instead, our automated Principal Component Interferometry (aPCI) approach only assumes that one can produce some linear combinations of the temporally nearby regularly spaced phase measurements, which cancel the laser noise. Then we let the data reveal those combinations, thus providing us with a set of laser-noise-free data channels. Our basic approach relied on the simplifying additional assumption that laser-noise-cancelling data combinations or the filters which lead to the laser-noise-free data streams are time-independent. In LISA, however, these filters will vary as the constellation armlengths evolve. Here, we discuss a generalization of the basic aPCI concept compatible with data dominated by a still unmodeled but slowly varying dominant noise covariance. We find that despite its independence on any model, the aPCI processing successfully mitigates laser frequency noise below the other noise sources level, and that its sensitivity to gravitational waves is the same as the state-of-the-art second-generation time-delay interferometry, up to a 2% error.Quentin Baghi, John G. Baker, Jacob Slutsky, James Ira Thorpework_z2fw2r62nzhphkywj72vi3u2r4Thu, 22 Sep 2022 00:00:00 GMTAn Additive Noise Approximation to Keller-Segel-Dean-Kawasaki Dynamics Part I: Local Well-Posedness of Paracontrolled Solutions
https://scholar.archive.org/work/c2o3qumoibfxbiibxsbvywwusq
Using the method of paracontrolled distributions, we show the local well-posedness of an additive noise approximation to the fluctuating hydrodynamics of the Keller-Segel model on the two-dimensional torus. Our approximation is a non-linear, non-local, parabolic-elliptic stochastic PDE with an irregular, heterogeneous space-time noise. As a consequence of the irregularity and heterogeneity, solutions to this equation must be renormalised by a sequence of diverging fields. Using the symmetry of the elliptic Green's function, which appears in our non-local term, we establish that the renormalisation diverges at most logarithmically, an improvement over the linear divergence one would expect by power counting. Similar cancellations also serve to reduce the number of diverging counterterms.Adrian Martini, Avi Mayorcaswork_c2o3qumoibfxbiibxsbvywwusqThu, 22 Sep 2022 00:00:00 GMTOn a multi-dimensional McKean-Vlasov SDE with memorial and singular interaction associated to the parabolic-parabolic Keller-Segel model
https://scholar.archive.org/work/tamunuky6zc7xb5xom2piq3ija
In this work we firstly prove the well-posedness of the non-linear martingale problem related to a McKean-Vlasov stochastic differential equation with singular interaction kernel in ℝ^d for d≥ 3. The particularity of our setting is that the McKean-Vlasov process we study interacts at each time with all its past time marginal laws by means of a singular space-time kernel. Secondly, we prove that our stochastic process is a probabilistic interpretation for the parabolic-parabolic Keller-Segel system in ℝ^d. We thus obtain a well-posedness result to the latter under explicit smallness condition on the parameters of the model.Milica Tomašević, Guillaume Woessnerwork_tamunuky6zc7xb5xom2piq3ijaThu, 22 Sep 2022 00:00:00 GMTEfficient Numerical Simulation of Soil-Tool Interaction
https://scholar.archive.org/work/abcs2uszmrdtdgmywcpj2vtuoa
The simulation of soil-tool interaction forces using the Discrete Element Method (DEM) is widely established. In addition to an acceptable prediction quality, the efficient simulation of granular material on high performance clusters with modern parallelization strategies for the industrial applications is indispensable. Although, for relevant problem sizes such simulations are so far not real-time capable. Further on, the inclusion of the human-machine interaction at a driving simulator combined with soil-tool simulation poses many interesting research questions. We therefore strive for sufficient performance and consider alternative models and algorithms to achieve real-time capability. First, we discuss different types of particle models regarding force accuracy and efficiency. The pros and cons are pointed out and the suitability for real-time applications is discussed. Second, we present two machine learning algorithms which are real-time capable and allow force predictions in real-time. The application at the in-house excavator simulator is discussed and the capability is shown using relevant numerical examples.Jonathan Jahnke, Fraunhofer-Gesellschaftwork_abcs2uszmrdtdgmywcpj2vtuoaWed, 21 Sep 2022 00:00:00 GMTHuman Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives
https://scholar.archive.org/work/4lgkodnscfchxnrpgnjepgsqfq
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Hao Li, Zeyu Tang, Yang Nan, Guang Yangwork_4lgkodnscfchxnrpgnjepgsqfqWed, 21 Sep 2022 00:00:00 GMTEssays on Dynamic Conditional Score Models and Breaks
https://scholar.archive.org/work/fg6blccezzcmtp2k7a36ubzzxy
In this dissertation I develop three essays on dynamic conditional score (DCS) models for univariate and multivariate models. In the first essay, I focus on a DCS model with a short memory process with changes in regimes for volatility. I also study volatility dynamics in my second essay using score-based copula models with time-varying dependencies with two components. For my last essay, I propose a score-driven multivariate model with factors for the location or mean of a set of macroeconometric variables. All my essays deal with episodes of atypical observations such as the global financial crisis and the recent pandemic.WILLY ARTURO ALANYA BELTRANwork_fg6blccezzcmtp2k7a36ubzzxyTue, 20 Sep 2022 00:00:00 GMTData-Driven Control of Stochastic Systems: An Innovation Estimation Approach
https://scholar.archive.org/work/fkcbnholozbhbismvecdrz6bru
Recent years have witnessed a booming interest in the data-driven paradigm for predictive control. However, under noisy data ill-conditioned solutions could occur, causing inaccurate predictions and unexpected control behaviours. In this article, we explore a new route toward data-driven control of stochastic systems through active offline learning of innovation data, which gives an answer to the critical question of how to derive an optimal data-driven model from a noise-corrupted dataset. A generalization of the Willems' fundamental lemma is developed for non-parametric representation of input-output-innovation trajectories, provided realizations of innovation are precisely known. This yields a model-agnostic unbiased output predictor and paves the way for data-driven receding horizon control, whose behaviour is identical to the "oracle" solution of certainty-equivalent model-based control with measurable states. For efficient innovation estimation, a new low-rank subspace identification algorithm is developed. Numerical simulations show that by actively learning innovation from input-output data, remarkable improvement can be made over present formulations, thereby offering a promising framework for data-driven control of stochastic systems.Yibo Wang, Chao Shang, Dexian Huangwork_fkcbnholozbhbismvecdrz6bruMon, 19 Sep 2022 00:00:00 GMTThe MIGDAL experiment: Measuring a rare atomic process to aid the search for dark matter
https://scholar.archive.org/work/ud46u6t5prazbook56qdonmpjq
We present the Migdal In Galactic Dark mAtter expLoration (MIGDAL) experiment aiming at the unambiguous observation and study of the so-called Migdal effect induced by fast-neutron scattering. It is hoped that this elusive atomic process can be exploited to enhance the reach of direct dark matter search experiments to lower masses, but it is still lacking experimental confirmation. Our goal is to detect the predicted atomic electron emission which is thought to accompany nuclear scattering with low, but calculable probability, by deploying an Optical Time Projection Chamber filled with a low-pressure gas based on CF_4. Initially pure CF_4 will be used, and then mixed with other elements employed by leading dark matter search technologies – including noble species, plus Si and Ge. High resolution track images generated by a Gas Electron Multiplier stack, together with timing information from scintillation and ionisation readout, will be used for 3D reconstruction of the characteristic event topology expected for this process – an arrangement of two tracks sharing a common vertex, with one belonging to a Migdal electron and the other to a nuclear recoil. Different energy-loss rate distributions along both tracks will be used as a powerful discrimination tool against background events. In this article we present the final design of the experiment, informed by extensive particle and track simulations and detailed estimations of signal and background rates. In pure CF_4 we expect to observe 8.9 (29.3) Migdal events per calendar day of exposure to an intense D-D (D-T) neutron generator beam at the NILE facility located at the Rutherford Appleton Laboratory (UK). With our assumptions, 5σ median discovery significance can be achieved in under one day with either generator.H. M. Araújo, S. N. Balashov, J. E. Borg. F. M. Brunbauer, C. Cazzaniga, C. D. Frost, F. Garcia, A. C. Kaboth, M. Kastriotou, I. Katsioulas, A. Khazov, H. Kraus, V. A. Kudryavtsev, S. Lilley, A. Lindote, D. Loomba, M. I. Lopes. E. Lopez Asamar, P. Luna Dapica. P. A. Majewski, T. Marley, C. McCabe, A. F. Mills, M. Nakhostin, T. Neep, F. Neves, K. Nikolopoulos, E. Oliveri, L. Ropelewski, E. Tilly, V. N. Solovov, T. J. Sumner, J. Tarrant. R. Turnley, M. G. D. van der Grinten, R. Veenhofwork_ud46u6t5prazbook56qdonmpjqMon, 19 Sep 2022 00:00:00 GMTDamage Identification in Fiber Metal Laminates using Bayesian Analysis with Model Order Reduction
https://scholar.archive.org/work/3tzqzioh3jbmthnq5w5yrfb7im
Fiber metal laminates (FML) are composite structures consisting of metals and fiber reinforced plastics (FRP) which have experienced an increasing interest as the choice of materials in aerospace and automobile industries. Due to a sophisticated built up of the material, not only the design and production of such structures is challenging but also its damage detection. This research work focuses on damage identification in FML with guided ultrasonic waves (GUW) through an inverse approach based on the Bayesian paradigm. As the Bayesian inference approach involves multiple queries of the underlying system, a parameterized reduced-order model (ROM) is used to closely approximate the solution with considerably less computational cost. The signals measured by the embedded sensors and the ROM forecasts are employed for the localization and characterization of damage in FML. In this paper, a Markov Chain Monte-Carlo (MCMC) based Metropolis-Hastings (MH) algorithm and an Ensemble Kalman filtering (EnKF) technique are deployed to identify the damage. Numerical tests illustrate the approaches and the results are compared in regard to accuracy and efficiency. It is found that both methods are successful in multivariate characterization of the damage with a high accuracy and were also able to quantify their associated uncertainties. The EnKF distinguishes itself with the MCMC-MH algorithm in the matter of computational efficiency. In this application of identifying the damage, the EnKF is approximately thrice faster than the MCMC-MH.Nanda Kishore Bellam Muralidhar, Carmen Gräßle, Natalie Rauter, Andrey Mikhaylenko, Rolf Lammering, Dirk A. Lorenzwork_3tzqzioh3jbmthnq5w5yrfb7imMon, 19 Sep 2022 00:00:00 GMTProgress on stochastic analytic continuation of quantum Monte Carlo data
https://scholar.archive.org/work/eahyejzztbcajetpcbh2qid6c4
We report multipronged progress on the stochastic averaging approach to numerical analytic continuation of quantum Monte Carlo data. With the sampled spectrum parametrized with delta-functions in continuous frequency space, a calculation of the configurational entropy lends support to a simple goodness-of-fit criterion for the optimal sampling temperature. To further investigate entropic effects, we compare spectra sampled in continuous frequency with results of amplitudes sampled on a fixed frequency grid. We demonstrate equivalences between sampling and optimizing spectral functions with the maximum-entropy approach with different forms of the entropy. These insights revise prevailing notions of the maximum-entropy method and its relationship to stochastic analytic continuation. We further explore various adjustable (optimized) constraints that allow sharp spectral features to be resolved, in particular at the lower frequency edge. The constraints, e.g., the location of the edge or the spectral weight of a quasi-particle peak, are optimized using a statistical criterion. We show that this method can correctly reproduce both narrow and broad quasi-particle peaks. We next introduce a parametrization for more intricate spectral functions with sharp edges, e.g., power-law singularities. Tests with synthetic data as well as with real simulation data for the spin-1/2 Heisenberg chain demonstrate that constrained sampling methods can reproduce spectral functions with sharp edge features at unprecedented fidelity. We present new results for S=1/2 Heisenberg 2-leg and 3-leg ladders to illustrate the ability of the methods to resolve spectral features arising from both elementary and composite excitations. Finally, we also propose how the methods developed here could be used as "pre processors" for analytic continuation by machine learning.Hui Shao, Anders W. Sandvikwork_eahyejzztbcajetpcbh2qid6c4Mon, 19 Sep 2022 00:00:00 GMT