167 research outputs found
An efficient surrogate model for emulation and physics extraction of large eddy simulations
In the quest for advanced propulsion and power-generation systems,
high-fidelity simulations are too computationally expensive to survey the
desired design space, and a new design methodology is needed that combines
engineering physics, computer simulations and statistical modeling. In this
paper, we propose a new surrogate model that provides efficient prediction and
uncertainty quantification of turbulent flows in swirl injectors with varying
geometries, devices commonly used in many engineering applications. The novelty
of the proposed method lies in the incorporation of known physical properties
of the fluid flow as {simplifying assumptions} for the statistical model. In
view of the massive simulation data at hand, which is on the order of hundreds
of gigabytes, these assumptions allow for accurate flow predictions in around
an hour of computation time. To contrast, existing flow emulators which forgo
such simplications may require more computation time for training and
prediction than is needed for conducting the simulation itself. Moreover, by
accounting for coupling mechanisms between flow variables, the proposed model
can jointly reduce prediction uncertainty and extract useful flow physics,
which can then be used to guide further investigations.Comment: Submitted to JASA A&C
Sequential Design with Mutual Information for Computer Experiments (MICE): Emulation of a Tsunami Model
Computer simulators can be computationally intensive to run over a large
number of input values, as required for optimization and various uncertainty
quantification tasks. The standard paradigm for the design and analysis of
computer experiments is to employ Gaussian random fields to model computer
simulators. Gaussian process models are trained on input-output data obtained
from simulation runs at various input values. Following this approach, we
propose a sequential design algorithm, MICE (Mutual Information for Computer
Experiments), that adaptively selects the input values at which to run the
computer simulator, in order to maximize the expected information gain (mutual
information) over the input space. The superior computational efficiency of the
MICE algorithm compared to other algorithms is demonstrated by test functions,
and a tsunami simulator with overall gains of up to 20% in that case
2016 International Land Model Benchmarking (ILAMB) Workshop Report
As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections
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Statistical mechanics in climate emulation: Challenges and perspectives
Climate emulators are a powerful instrument for climate modeling, especially in terms of reducing the computational load for simulating spatiotemporal processes associated with climate systems. The most important type of emulators are statistical emulators trained on the output of an ensemble of simulations from various climate models. However, such emulators oftentimes fail to capture the “physics” of a system that can be detrimental for unveiling critical processes that lead to climate tipping points. Historically, statistical mechanics emerged as a tool to resolve the constraints on physics using statistics. We discuss how climate emulators rooted in statistical mechanics and machine learning can give rise to new climate models that are more reliable and require less observational and computational resources. Our goal is to stimulate discussion on how statistical climate emulators can further be improved with the help of statistical mechanics which, in turn, may reignite the interest of statistical community in statistical mechanics of complex systems
A gaussian process emulator for estimating the volume of tissue activated during deep brain stimulation
The volume of tissue activated (VTA) is a well-established approach to model the direct effects of deep brain stimulation (DBS) on neural tissue and previous studies have pointed to its potential clinical applications. However, the elevated computational time required to estimate the VTA with standard techniques used in biological neural modeling limits its suitability for practical use. The goal of this project was to develop
a novel methodology to reduce the computation time of VTA estimation. To that end, we built a Gaussian process emulator. It combines a field of multi-compartment axon models coupled to the stimulating electric field with a Gaussian process classifier (GPC); following the premise that computing the VTA from a field of axons is in essence a binary classification problem. We achieved a considerable reduction in the average
time required to estimate the VTA, under both ideal isotropic and realistic anisotropic brain tissue conductive
conditions, limiting the loss of accuracy and overcoming other drawbacks entailed by alternative methods
Attention U-Net as a surrogate model for groundwater prediction
Numerical simulations of groundwater flow are used to analyze and predict the
response of an aquifer system to its change in state by approximating the
solution of the fundamental groundwater physical equations. The most used and
classical methodologies, such as Finite Difference (FD) and Finite Element (FE)
Methods, use iterative solvers which are associated with high computational
cost. This study proposes a physics-based convolutional encoder-decoder neural
network as a surrogate model to quickly calculate the response of the
groundwater system. Holding strong promise in cross-domain mappings,
encoder-decoder networks are applicable for learning complex input-output
mappings of physical systems. This manuscript presents an Attention U-Net model
that attempts to capture the fundamental input-output relations of the
groundwater system and generates solutions of hydraulic head in the whole
domain given a set of physical parameters and boundary conditions. The model
accurately predicts the steady state response of a highly heterogeneous
groundwater system given the locations and piezometric head of up to 3 wells as
input. The network learns to pay attention only in the relevant parts of the
domain and the generated hydraulic head field corresponds to the target samples
in great detail. Even relative to coarse finite difference approximations the
proposed model is shown to be significantly faster than a comparative
state-of-the-art numerical solver, thus providing a base for further
development of the presented networks as surrogate models for groundwater
prediction
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