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International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

作者： Punzo, Vincenzo Montanino, Marcello Univ Naples Federico II Dept Civil Environm & Architectural Engn Naples Italy

ISBN: (纸本)9789633131428

The paper investigates the impact of different sampling strategies of car-following and lane-changing model parameters on traffic simulation results. The investigation considered seven possible sampling strategies including sampling parameters from independent normal distributions, which is customarily in commercial simulation software. Study results revealed that model performances in case of sampling from normal pdfs are extremely poor. In turn, results proved that parameter correlation, as well as the parameter distribution model, entail a big impact on model performances and should be properly take into account in the microsimulation practice.

关键词： calibration driver heterogeneity parameter sampling traffic micro-simulation

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Generation of Concrete parameters from Logical Urban Driving Scenarios Based on Hybrid Graphs 9

Generation of Concrete Parameters from Logical Urban Driving...

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9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS)

作者： Glasmacher, Christoph Weber, Hendrik Schuldes, Michael Wagener, Nicolas Eckstein, Lutz Rhein Westfal TH Aachen Inst Automot Engn Aachen Germany

ISBN: (纸本)9789897586521

Safety assurance of highly automated driving functions is a major challenge in today's research and requires the development of new validation methods. Scenario-based testing is a promising approach to handle the variety of possible situations efficiently. Due to the limited availability of real-world derived scenarios, they are increasingly generated synthetically. Whereas actual approaches to generate concrete parameters are mostly either knowledge- or data-driven, we propose a methodology to combine these approaches. We model the correlation of parameters in real-world data as multivariate probability functions by using copulas. In addition, we establish modular causal and constraint relations combining Bayesian networks and constraint graphs to add semantic knowledge about parameters and their interactions. Thereby, road user behavior and physical equations are represented. The application of our generation method on urban intersections shows the capability to sample high-dimensional parameter spaces with limited input data. Hereby, it offers the opportunity to create realistic scenarios to extend the database for scenario-based assessment.

关键词： Automated Driving Intelligent Vehicles Safety Assurance Scenario Generation parameter sampling Causal Networks Constraint Graphs

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Evaluation of Uncertainty in Stream Flow Prediction Using Monte Carlo Simulation for Watershed-Scale Hydrological Modeling

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JOURNAL OF HYDROLOGIC ENGINEERING 2024年第1期29卷 05023026-05023026页

作者： Dey, Preyanka Roy, Shuvashish Bathi, Jejal R. Mishra, Anurag WSP USA Environm & Infrastruct Inc 216 Centerview DrSuite 300 Brentwood TN 37027 USA Dynam Solut LLC 6421 Deane Hill DrSuite 1 Knoxville TN 37919 USA Univ Tennessee Dept Civil & Chem Engn Chattanooga TN 37403 USA Dynam Solut Int LLC POB 1916 Edmonds WA 98020 USA

Uncertainty analysis is crucial for the quality control and application of hydrological models in water resources management. For improving the reliability of model outcomes, the identification of uncertainties originating from the knowledge gap and stochastic behavior of the model is required. To demonstrate a detailed investigation of uncertainty in hydrological model prediction, we developed a Hydrological Simulation Program-FORTRAN (HSPF) model for simulating streamflow at the South Chickamauga Creek watershed, Tennessee, and quantified uncertainty in the model outputs resulting from the input parameters using Monte Carlo simulation. Additionally, we compared the relative efficiency of Latin hypercube (LH) and Monte Carlo (MC) methods used for parameter distribution for the Monte Carlo simulation. Both methods produced similar results, where annual average runoff had the lowest, peak flow had the medium, and low flow had the highest level of uncertainty. Notably, our investigation revealed that extreme flow conditions exhibited higher uncertainty in prediction. Findings from this study will help in better interpretation of model results to devise more informed watershed management decisions.

关键词： Hydrological modeling Uncertainty analysis parameter sampling Monte Carlo (MC) simulation

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Model Reduction Framework with a New Take on Active Subspaces for Optimization Problems with Linearized Fluid-Structure Interaction Constraints

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2021年第19期122卷 5450-5481页

作者： Boncoraglio, Gabriele Farhat, Charbel Bou-Mosleh, Charbel Stanford Univ Dept Aeronaut & Astronaut Durand Bldg496 Lomita Mall Stanford CA 94305 USA Stanford Univ Dept Mech Engn Bldg 530440 Escondido Mall Stanford CA 94305 USA Stanford Univ Inst Computat & Math Engn Huang Engn Ctr 475 Via OrtegaSuite 060 Stanford CA 94305 USA Notre Dame Univ Louaize Dept Mech Engn Zouk Mosbeh Lebanon

In this paper, a new take on the concept of an active subspace for reducing the dimension of the design parameter space in a multidisciplinary analysis and optimization (MDAO) problem is proposed. The new approach is intertwined with the concepts of adaptive parameter sampling, projection-based model order reduction, and a database of linear, projection-based reduced-order models equipped with interpolation on matrix manifolds, in order to construct an efficient computational framework for MDAO. The framework is fully developed for MDAO problems with linearized fluid-structure interaction constraints. It is applied to the aeroelastic tailoring, under flutter constraints, of two different flight systems: a flexible configuration of NASA's Common Research Model;and NASA's Aeroelastic Research Wing #2 (ARW-2). The obtained results illustrate the feasibility of the computational framework for realistic MDAO problems and highlight the benefits of the new approach for constructing an active subspace in both terms of solution optimality and wall-clock time reduction.

关键词： active subspace constrained optimization fluid-structure interaction gradient-based optimization interpolation on matrix manifolds model reduction parameter sampling

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Ten strategies towards successful calibration of environmental models

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JOURNAL OF HYDROLOGY 2023年第PartA期620卷

作者： Mai, Juliane Univ Waterloo Civil & Environm Engn 200 Univ Ave West Waterloo ON N2L 3G1 Canada UFZ Helmholtz Ctr Environm Res Computat Hydrosyst Permoser Str 15 D-04318 Leipzig Saxony Germany Artificial Intelligence ScaDS AI Ctr Scalable Data Analyt Humboldtstr 25 D-04105 Leipzig Saxony Germany

Model calibration is the procedure of finding model settings such that simulated model outputs best match the observed data. Model calibration is necessary when the model parameters cannot directly be measured as is the case with a wide range of environmental models where parameters are conceptually describing upscaled and effective physical processes. Model calibration is therefore an important step of environmental modeling as the model might otherwise provide random outputs if never compared to a ground truth. Model calibration itself is often referred to be an art due to its plenitude of intertwined steps and necessary decisions along the way before a calibration can be carried out or can be regarded successful. This work provides a general guide specifying which steps a modeler needs to undertake, how to diagnose the success of each step, and how to identify the right action to revise steps that were not successful. The procedure is formalized into ten iterative steps generally appearing in calibration experiments. Each step of this "calibration life cycle"is either illustrated with an exemplary calibration experiment or providing an explicit checklist the modeler can follow. These ten strategies are: (1) using sensitivity information to guide the calibration, (2) handling of parameters with constraints, (3) handling of data ranging orders of magnitude, (4) choosing the data to base the calibration on, (5) presenting various methods to sample model parameters, (6) finding appropriate parameter ranges, (7) choosing objective functions, (8) selecting a calibration algorithm, (9) determining the success and quality of a multi-objective calibration, and (10) providing a checklist to diagnose calibration performance using ideas introduced in the previous steps. The formal definition of strategies through the calibration process is providing an overview while shedding a light on connections between these main ingredients to calibrate an environmental model and will t

关键词： Model calibration parameter sampling Calibration strategies

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PIECEWISE-GLOBAL NONLINEAR MODEL ORDER REDUCTION FOR PDE-CONSTRAINED OPTIMIZATION IN HIGH-DIMENSIONAL parameter SPACES

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SIAM JOURNAL ON SCIENTIFIC COMPUTING 2022年第4期44卷 A2176-A2203页

作者： Boncoraglio, Gabriele Farhat, Charbel Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA Stanford Univ Inst Computat & Math Engn Stanford CA 94305 USA

This paper introduces the concept of a database of piecewise-global, nonlinear, projection-based reduced-order models and intertwines it with that of an active subspace. The result is a computational framework for accelerating the solution of optimization problems in a design parameter space of a relatively large dimension. On the algorithmic side, the scope of this framework includes multistart, gradient-based optimization methods and hyperreduced nonlinear models. On the application side, it includes multi-objective and global optimization problems, as well as nonlinear, PDE-based constraints. The potential of the proposed computational framework for optimization is demonstrated using aerodynamic shape optimization problems associated with two different flight systems: the flying wing aircraft mAEWing2, and the NASA Common Research Model (CRM) for transport aircraft.

关键词： active subspace gradient-based optimization hyperreduction nonlinear model reduction parameter sampling PDE-constrained optimization

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In situ adaptive reduction of nonlinear multiscale structural dynamics models

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 2020年第22期121卷 4971-4988页

作者： He, Wanli Avery, Philip Farhat, Charbel Stanford Univ Dept Mech Engn Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Durand Bldg496 Lomita Mall Stanford CA 94305 USA Huang Engn Ctr Inst Computat & Math Engn Stanford CA USA

Conventional offline training of reduced-order bases in a predetermined region of a parameter space leads to parametric reduced-order models that are vulnerable to extrapolation. This vulnerability manifests itself whenever a queried parameter point lies in an unexplored region of the parameter space. This article addresses this issue by presenting an in situ, adaptive framework for nonlinear model reduction where computations are performed by default online and shifted offline as needed. The framework is based on the concept of a database of local reduced-order bases (ROBs), where locality is defined in the parameter space of interest. It achieves accuracy by updating on-the-fly a precomputed ROB and approximating the solution of a dynamical system along its trajectory using a sequence of most appropriate local ROBs. It achieves efficiency by managing the dimension of a local ROB and incorporating hyperreduction in the process. While sufficiently comprehensive, the framework is described in the context of dynamic multiscale computations in solid mechanics. In this context, even in a nonparametric setting of the macroscale problem and when all offline, online, and adaptation overhead costs are accounted for, the proposed computational framework can accelerate a single three-dimensional, nonlinear, multiscale computation by an order of magnitude, without compromising accuracy.

关键词： dynamics hyperreduction local reduced-order basis multiscale nonlinear model reduction parameter sampling

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Mechanistic and data-driven models of cell signaling: Tools for fundamental discovery and rational design of therapy

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CURRENT OPINION IN SYSTEMS BIOLOGY 2021年 28卷 100349页

作者： Myers, Paul J. Lee, Sung Hyun Lazzara, Matthew J. Univ Virginia Dept Chem Engn Charlottesville VA 22904 USA Univ Virginia Dept Biomed Engn Charlottesville VA 22904 USA

A full understanding of cell signaling processes requires knowledge of protein structure???function relationships, protein???protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and be-tween multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete represen-tations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

关键词： Uncertainty Sensitivity parameter sampling parameter estimation Regression Clustering Classification Cancer Immunology

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Kinetic modeling of the Calvin cycle identifies flux control and stable metabolomes in Synechocystis carbon fixation

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JOURNAL OF EXPERIMENTAL BOTANY 2019年第3期70卷 973-983页

作者： Janasch, Markus Asplund-Samuelsson, Johannes Steuer, Ralf Hudson, Elton P. KTH Royal Inst Technol Sch Engn Sci Chem Biotechnol & Hlth Sci Life Lab POB 1031 S-17121 Solna Sweden Humboldt Univ Inst Biol Fachinst Theoret Biol ITB Berlin Germany

Biological fixation of atmospheric CO2 via the Calvin-Benson-Bassham cycle has massive ecological impact and offers potential for industrial exploitation, either by improving carbon fixation in plants and autotrophic bacteria, or by installation into new hosts. A kinetic model of the Calvin-Benson-Bassham cycle embedded in the central carbon metabolism of the cyanobacterium Synechocystis sp. PCC 6803 was developed to investigate its stability and underlying control mechanisms. To reduce the uncertainty associated with a single parameter set, random sampling of the steady-state metabolite concentrations and the enzyme kinetic parameters was employed, resulting in millions of parameterized models which were analyzed for flux control and stability against perturbation. Our results show that the Calvin cycle had an overall high intrinsic stability, but a high concentration of ribulose 1,5-bisphosphate was associated with unstable states. Low substrate saturation and high product saturation of enzymes involved in highly interconnected reactions correlated with increased network stability. Flux control, that is the effect that a change in one reaction rate has on the other reactions in the network, was distributed and mostly exerted by energy supply (ATP), but also by cofactor supply (NADPH). Sedoheptulose 1,7-bisphosphatase/fructose 1,6-bisphosphatase, fructose-bisphosphate aldolase, and transketolase had a weak but positive effect on overall network flux, in agreement with published observations. The identified flux control and relationships between metabolite concentrations and system stability can guide metabolic engineering. The kinetic model structure and parameterizing framework can be expanded for analysis of metabolic systems beyond the Calvin cycle.

关键词： Calvin cycle cyanobacteria flux control kinetic model metabolic engineering metabolic model metabolome parameter sampling system stability

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Gradient-based constrained optimization using a database of linear reduced-order models

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JOURNAL OF COMPUTATIONAL PHYSICS 2020年 423卷 109787-109787页

作者： Choi, Youngsoo Boncoraglio, Gabriele Anderson, Spenser Amsallem, David Farhat, Charbel Lawrence Livermore Natl Lab Computat Engn Div Livermore CA 94550 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA Stanford Univ Dept Mech Engn Stanford CA 94305 USA Stanford Univ Inst Computat & Math Engn Stanford CA 94305 USA Facebook Menlo Pk CA 94025 USA

A methodology grounded in model reduction is presented for accelerating the gradient-based solution of a family of linear or nonlinear constrained optimization problems where the constraints include at least one linear Partial Differential Equation (PDE). A key component of this methodology is the construction, during an offline phase, of a database of pointwise, linear, Projection-based Reduced-Order Models (PROM)s associated with a design parameter space and the linear PDE(s). A parameter sampling procedure based on an appropriate saturation assumption is proposed to maximize the efficiency of such a database of PROMs. A real-time method is also presented for interpolating at any queried but unsampled parameter vector in the design parameter space the relevant sensitivities of a PROM. The practical feasibility, computational advantages, and performance of the proposed methodology are demonstrated for several realistic, nonlinear, aerodynamic shape optimization problems governed by linear aeroelastic constraints. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Constrained optimization Flutter Gradient-based optimization Interpolation on a matrix manifold Model reduction parameter sampling

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