Gotowe bibliografie tematyczne / Multi-fidelity models

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Gotowa bibliografia na temat „Multi-fidelity models”

Autor: Grafiati

Data publikacji: 29 sierpnia 2023

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Artykuły w czasopismach na temat "Multi-fidelity models"

Razi, Mani, Robert M. Kirby, and Akil Narayan. "Fast predictive multi-fidelity prediction with models of quantized fidelity levels." Journal of Computational Physics 376 (January 2019): 992–1008. http://dx.doi.org/10.1016/j.jcp.2018.10.025.

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Perdikaris, P., M. Raissi, A. Damianou, N. D. Lawrence, and G. E. Karniadakis. "Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 473, no. 2198 (2017): 20160751. http://dx.doi.org/10.1098/rspa.2016.0751.

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Multi-fidelity modelling enables accurate inference of quantities of interest by synergistically combining realizations of low-cost/low-fidelity models with a small set of high-fidelity observations. This is particularly effective when the low- and high-fidelity models exhibit strong correlations, and can lead to significant computational gains over approaches that solely rely on high-fidelity models. However, in many cases of practical interest, low-fidelity models can only be well correlated to their high-fidelity counterparts for a specific range of input parameters, and potentially return wrong trends and erroneous predictions if probed outside of their validity regime. Here we put forth a probabilistic framework based on Gaussian process regression and nonlinear autoregressive schemes that is capable of learning complex nonlinear and space-dependent cross-correlations between models of variable fidelity, and can effectively safeguard against low-fidelity models that provide wrong trends. This introduces a new class of multi-fidelity information fusion algorithms that provide a fundamental extension to the existing linear autoregressive methodologies, while still maintaining the same algorithmic complexity and overall computational cost. The performance of the proposed methods is tested in several benchmark problems involving both synthetic and real multi-fidelity datasets from computational fluid dynamics simulations.

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Rumpfkeil, Markus P., Dean Bryson, and Phil Beran. "Multi-Fidelity Sparse Polynomial Chaos and Kriging Surrogate Models Applied to Analytical Benchmark Problems." Algorithms 15, no. 3 (2022): 101. http://dx.doi.org/10.3390/a15030101.

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In this article, multi-fidelity kriging and sparse polynomial chaos expansion (SPCE) surrogate models are constructed. In addition, a novel combination of the two surrogate approaches into a multi-fidelity SPCE-Kriging model will be presented. Accurate surrogate models, once obtained, can be employed for evaluating a large number of designs for uncertainty quantification, optimization, or design space exploration. Analytical benchmark problems are used to show that accurate multi-fidelity surrogate models can be obtained at lower computational cost than high-fidelity models. The benchmarks include non-polynomial and polynomial functions of various input dimensions, lower dimensional heterogeneous non-polynomial functions, as well as a coupled spring-mass-system. Overall, multi-fidelity models are more accurate than high-fidelity ones for the same cost, especially when only a few high-fidelity training points are employed. Full-order PCEs tend to be a factor of two or so worse than SPCES in terms of overall accuracy. The combination of the two approaches into the SPCE-Kriging model leads to a more accurate and flexible method overall.

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DiazDelaO, F. A., and S. Adhikari. "Bayesian assimilation of multi-fidelity finite element models." Computers & Structures 92-93 (February 2012): 206–15. http://dx.doi.org/10.1016/j.compstruc.2011.11.002.

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Rumpfkeil, Markus P., and Philip Beran. "Multi-fidelity surrogate models for flutter database generation." Computers & Fluids 197 (January 2020): 104372. http://dx.doi.org/10.1016/j.compfluid.2019.104372.

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Bonomo, Anthony L. "Multi-fidelity surrogate modeling for structural acoustics applications." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A287. http://dx.doi.org/10.1121/10.0018869.

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Recently, surrogate modeling methods have been explored for structural acoustics applications. These often involve evaluation of an “expensive” high-fidelity computational model to obtain training data. However, in many applications, models of varying fidelity and computational cost are available. In such situations, one can leverage multi-fidelity surrogate modeling, where the training data from models of varying fidelity are combined and simultaneously used to produce a surrogate model. A particularly popular class of multi-fidelity surrogate modeling techniques is known as co-Kriging, where simulation output from both “expensive” and “cheap” computational models are correlated and a correction process is obtained that maps between the results of these models of varying fidelity. This talk will review co-Kriging and demonstrate its utility on a canonical structural acoustics problem. [Work supported by the Office of Naval Research.]

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Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Selection of Existing Sail Designs for Multi-Fidelity Surrogate Models." Journal of Sailing Technology 7, no. 01 (2022): 31–51. http://dx.doi.org/10.5957/jst/2022.7.2.31.

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Velocity Prediction Programs (VPPs) are commonly used to help predict and compare the performance of different sail designs. A VPP requires an aerodynamic input force matrix which can be computationally expensive to calculate, limiting its application in industrial sail design projects. The use of multi-fidelity kriging surrogate models has previously been presented by the authors to reduce this cost, with high-fidelity data for a new sail being modelled and the low-fidelity data provided by data from existing, but different, sail designs. The difference in fidelity is not due to the simulation method used to obtain the data, but instead how similar the sail’s geometry is to the new sail design. An important consideration for the construction of these models is the choice of low-fidelity data points, which provide information about the trend of the model curve between the high-fidelity data. A method is required to select the best existing sail design to use for the low-fidelity data when constructing a multi-fidelity model. The suitability of an existing sail design as a low fidelity model could be evaluated based on the similarity of its geometric parameters with the new sail. It is shown here that for upwind jib sails, the similarity of the broadseam between the two sails best indicates the ability of a design to be used as low-fidelity data for a lift coefficient surrogate model. The lift coefficient surrogate model error predicted by the regression is shown to be close to 1% of the lift coefficient surrogate error for most points. Larger discrepancies are observed for a drag coefficient surrogate error regression.

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Peart, Tanya, Nicolas Aubin, Stefano Nava, John Cater, and Stuart Norris. "Multi-Fidelity Surrogate Models for VPP Aerodynamic Input Data." Journal of Sailing Technology 6, no. 01 (2021): 21–43. http://dx.doi.org/10.5957/jst/2021.6.1.21.

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Predicting the performance of a sail design is important for optimising the performance of a yacht, and Velocity Prediction Programs (VPPs) are commonly used for this purpose. The aerodynamic force data for a VPP is often calculated using Computational Fluid Dynamics (CFD) models, but these can be computationally expensive. A full VPP analysis for sail design is therefore usually restricted to high-budget design projects or research activities and is not practical for many industry projects. This work presents a method to reduce the computational cost of creating lift and drag force coefficient curves for input into a VPP using both multi-fidelity kriging surrogate modelling and data from existing sail designs. This method is shown to reduce the number of CFD simulations required for a desired accuracy when compared to a single-fidelity model. A maximum reduction in the required computational effort of 57% was achieved for model-scale symmetric spinnaker sails. For the same number of simulations, the accuracy of the model predictions was improved by up to 72% for scale-symmetric spinnaker sails, and 90% for asymmetric spinnakers.

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Farcaș, Ionuț-Gabriel, Benjamin Peherstorfer, Tobias Neckel, Frank Jenko, and Hans-Joachim Bungartz. "Context-aware learning of hierarchies of low-fidelity models for multi-fidelity uncertainty quantification." Computer Methods in Applied Mechanics and Engineering 406 (March 2023): 115908. http://dx.doi.org/10.1016/j.cma.2023.115908.

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Styler, Breelyn, and Reid Simmons. "Plan-Time Multi-Model Switching for Motion Planning." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 558–66. http://dx.doi.org/10.1609/icaps.v27i1.13858.

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Robot navigation through non-uniform environments requires reliable motion plan generation. The choice of planning model fidelity can significantly impact performance. Prior research has shown that reducing model fidelity saves planning time, but sacrifices execution reliability. While current adaptive hierarchical motion planning techniques are promising, we present a framework that leverages a richer set of robot motion models at plan-time. The framework chooses when to switch models and what model is most applicable within a single trajectory. For instance, more complex environment locales require higher fidelity models, while lower fidelity models are sufficient for simpler parts of the planning space, thus saving plan time. Our algorithm continuously aims to pick the model that best handles the current local environment. This effectively generates a single, mixed-fidelity plan. We present results for a simulated mobile robot with attached trailer in a hospital domain. We compare using a single motion planning model to switching with our framework of multiple models. Our results demonstrate that multi-fidelity model switching increases plan-time efficiency without sacrificing execution reliability.

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