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Surrogate modeling in structural vibration problems with dynamic mode decomposition
The Journal of the Acoustical Society of America, 2022Solving large-scale vibration problems presents a difficult challenge for complicated geometries that is only addressable through the use of large-scale compute resources. These problems not only require extensive compute time and cost, but are also expensive in engineering hours for modeling and analysis.
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A Dynamic Sampling Method for Kriging and Cokriging Surrogate Models
49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, 2011In this paper we describe our gradient and Hessian enhanced Kriging surrogate model with dynamic sample point selection. We demonstrate the quality of the surrogate by comparison with higher-dimensional analytic test functions. We also apply the surrogate model to uncertainty quantification and robust optimization problems using inexpensive Monte-Carlo
Markus Rumpfkeil +2 more
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ACTIVE DEEP SURROGATE MODEL FOR SPATIO-TEMPORAL EPIDEMIC DYNAMICS
International Conference on Modern Problems of Mathematics, Mechanics and their ApplicationsAbstract. Epidemiology involves the study of how diseases spread and can be controlled in populations. In this work, we present a new data-driven deep learning method for parametric approximate solutions of spatio-temporal Susceptible-InfectedRecovered (SIR) models. Traditional SIR models focus on temporal dynamics and often neglect spatial variability,
Emel Kurul +3 more
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Data-Driven Surrogate Models for Computational Fluid Dynamics
The use of computational fluid dynamics (CFD) has become essential for aerospace design optimization processes. The computational cost of high-fidelity CFD is often very large and can make design optimization prohibitively expensive if a large number of design evaluations are required.openaire +1 more source
Meteorologiya i Gidrologiya
The paper examines a methodological approach to simulating nonlinear atmospheric dynamics. The approach implies constructing a surrogate (replacement) model of a physical object (process) based on machine learning. For illustrative purposes, the surrogate model is built for the conceptual model of the coupled ocean-atmosphere system, in which the ...
S. A. Soldatenko, Ya. I. Angudovich
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The paper examines a methodological approach to simulating nonlinear atmospheric dynamics. The approach implies constructing a surrogate (replacement) model of a physical object (process) based on machine learning. For illustrative purposes, the surrogate model is built for the conceptual model of the coupled ocean-atmosphere system, in which the ...
S. A. Soldatenko, Ya. I. Angudovich
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Predicting Glacier Dynamics with Neural Operator-Based Surrogate Models.
Traditional numerical solvers for simulating glacier dynamics are computationally demanding, particularly for large-scale and long-period projections. Recent use of neural networks(NNs) based surrogate models, including data-driven and physics-informed convolutional neural networks (CNNs), have shown considerable success in accelerating simulation ...Mamta K C +2 more
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
exaly
Demand response scheduling using derivative-based dynamic surrogate models
Computers & Chemical Engineering, 2022Alessandro Di Pretoro +4 more
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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Surrogate modeling of vehicle dynamics using deep learning
The Proceedings of Design & Systems Conference, 2019Kohei MAKINO +4 more
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