Emulation of physical processes with Emukit

Paleyes, Andrei; Pullin, Mark; Mahsereci, Maren; McCollum, Cliff; Lawrence, Neil D.; González, J. G.

doi:10.48550/arxiv.2110.13293

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…First, GPR models have relatively high accuracy [1]. Further, this method allows the combining of results of different fidelities or even the combining of simulation and experimental data [1,2,6]. GPR can also be tailored to specific problems by selecting different basis functions and can be tuned to take best advantage of sparse data [7].…”

Section: Methodsmentioning

confidence: 99%

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Application of multi-fidelity methods to rotorcraft performance assessment

Walizer,

Haehnel,

Allen

et al. 2024

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We present a Python-based multi-fidelity tool to estimate rotorcraft performance metrics. We use Gaussian-Process regression (GPR) methods to adaptively build a surrogate model using a small number of high-fidelity CFD points to improve estimates of performance metrics from a medium-fidelity comprehensive analysis model. To include GPR methods in our framework, we used the EmuKit Python package. Our framework adaptively chooses new high-fidelity points to run in regions where the model variance is high. These high-fidelity points are used to update the GPR model; convergence is reached when model variance is below a pre-determined level. To efficiently use our framework on large computer clusters, we implemented this in Galaxy Simulation Builder, an analysis tool that is designed to work on large parallel computing environments. The program is modular, and is designed to be agnostic to the number and names of dependent variables and to the number and identifying labels of the fidelity levels. We demonstrate our multi-fidelity modeling framework on a rotorcraft collective sweep (hover) simulation and compare the accuracy and time savings of the GPR model to that of a simulation run with CFD only.

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Section: Methodsmentioning

confidence: 99%

“…EmuKit [6] is a publicly available Python package that provides simple tools to create GPR models and provides an easy way to integrate MF methods into our Python-based framework. Our objective is to create an adaptive framework that uses high-fidelity simulations sparingly as needed to improve the MF accuracy.…”

Section: Methodsmentioning

confidence: 99%

Application of multi-fidelity methods to rotorcraft performance assessment

Walizer,

Haehnel,

Allen

et al. 2024

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“…We train the GP emulators separately for each redshift. We implement our multi-fidelity models using Emukit [84].…”

Section: Gaussian Process Emulatorsmentioning

confidence: 99%

Cosmological constraints from the eBOSS Lyman-α forest using the PRIYA simulations

Fernandez,

Bird,

2024

J. Cosmol. Astropart. Phys.

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We present new cosmological parameter constraints from the eBOSS Lyman-α forest survey. We use a new theoretical model and likelihood based on the PRIYA simulation suite. PRIYA is the first suite to resolve the Lyman-α forest in a (120 Mpc/h)3 volume, using a multi-fidelity emulation technique. We use PRIYA to predict Lyman-α forest observables with ≲ 1% interpolation error over an 11 dimensional (9 simulated, 2 in post-processing) parameter space. We identify an internal tension within the flux power spectrum data. Once the discrepant data is removed, we find the primeval scalar spectral index measured at a pivot scale of k 0 = 0.78 Mpc-1 to be nP = 1.009+0.027 -0.018 at 68% confidence. This measurement from the Lyman-α forest flux power spectrum alone is in reasonable agreement with Planck, and in tension with earlier eBOSS analyses. The amplitude of matter fluctuations is σ 8 = 0.733+0.026 -0.029 at 68% confidence, in agreement with Dark Energy Survey weak lensing measurements and other small-scale structure probes and in tension with CMB measurements from Planck and ACT. The effective optical depth to Lyman-α photons from our pipeline is in good agreement with earlier high resolution measurements. We find a linear power at z = 3 and k = 0.009 s/km of Δ2 L = 0.302+0.024 -0.027 with a slope n eff = -2.264+0.026 -0.018. Our flux power spectrum only chains prefer a low level of heating during helium reionization. When we add IGM temperature data we find nP = 0.983 ± 0.020 and σ 8 = 0.703+0.023 -0.027. Our chains prefer an early and long helium reionization event, as suggested by measurements from the helium Lyman-α forest. In the near future we will use our pipeline to infer cosmological parameters from the DESI Lyman-α data.

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“…We use the GPy package [103] and EmuKit [104]. If P F (θ) is the simulated Lymanα forest flux power spectrum as a function of a parameter vector θ, then a GP models this output as draws from a distribution…”

Section: Multi-fidelity Emulatormentioning

confidence: 99%

PRIYA: a new suite of Lyman-α forest simulations for cosmology

Bird,

Fernandez,

et al. 2023

J. Cosmol. Astropart. Phys.

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We present the PRIYA suite of cosmological simulations, based on the code and hydrodynamic model of the ASTRID simulation, and designed for cosmological analyses of the Lyman-α forest. Our simulation suite spans a 9-dimensional parameter space, including 4 cosmological parameters and 5 astrophysical/thermal parameters. We have run 48 low fidelity simulations with 15363 particles in a 120 Mpc/h box and 3 high fidelity simulations with 30723 particles in a 120 Mpc/h box. All our simulations include a full physics model for galaxy formation, including supernova and AGN feedback, and thus also contain a realistic population of DLAs. We advance on earlier simulations suites by larger particle loads, by incorporating new physical models for patchy hydrogen and helium reionization, and by self-consistently incorporating a model for AGN feedback. We show that patchy helium reionization imprints an excess in the 1D flux power spectrum on large scales, which may allow future measurements of helium reionization bubble sizes. Simulation parameters are chosen based on a Latin hypercube design and a Gaussian process is used to interpolate to arbitrary parameter combinations. We build a multi-fidelity emulator for the 1D flux power spectrum and the mean IGM temperature. We show that our final interpolation error is < 1% and that our simulations produce a flux power spectrum converged at the percent level for z = 5.4–2.2. Our simulation suite will be used to interpret Lyman-α forest 1D flux power spectra from SDSS and future DESI data releases.

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Emulation of physical processes with Emukit

Cited by 4 publications

References 16 publications

Application of multi-fidelity methods to rotorcraft performance assessment

Application of multi-fidelity methods to rotorcraft performance assessment

Cosmological constraints from the eBOSS Lyman-α forest using the PRIYA simulations

PRIYA: a new suite of Lyman-α forest simulations for cosmology

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