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Meta-Model Framework for Surrogate-Based Parameter Estimation in Dynamical Systems
The central task in modeling complex dynamical systems is parameter esti...
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Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques
Improving predictive understanding of Earth system variability and chang...
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Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and machine learning techniques
In this work, we consider two kinds of model reduction techniques to sim...
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Numerical modelling of a peripheral arterial stenosis using dimensionally reduced models and kernel methods
In this work, we consider two kinds of model reduction techniques to sim...
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Model Order Reduction for Rotating Electrical Machines
The simulation of electric rotating machines is both computationally exp...
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MFNets: Learning network representations for multifidelity surrogate modeling
This paper presents an approach for constructing multifidelity surrogate...
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Deep Surrogate Models for Multi-dimensional Regression of Reactor Power
There is renewed interest in developing small modular reactors and micro...
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A fast time-stepping strategy for ODE systems equipped with a surrogate model
Simulation of complex dynamical systems arising in many applications is computationally challenging due to their size and complexity. Model order reduction, machine learning, and other types of surrogate modeling techniques offer cheaper and simpler ways to describe the dynamics of these systems, but are inexact and introduce additional approximation errors. In order to overcome the computational difficulties of the full complex models, on one hand, and the limitations of surrogate models, on the other, this work proposes a new accelerated time-stepping strategy that combines information from both. This approach is based on the multirate infinitesimal general-structure additive Runge-Kutta (MRI-GARK) framework. The inexpensive surrogate model is integrated with a small timestep to guide the solution trajectory, and the full model is treated with a large timestep to occasionally correct for the surrogate model error and ensure convergence. We provide a theoretical error analysis, and several numerical experiments, to show that this approach can be significantly more efficient than using only the full or only the surrogate model for the integration.
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