Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

Christopher, Jason D.; Doronina, Olga A.; Petrykowski, Dan; Hayden, Torrey R. S.; Lapointe, Caelan; Wimer, Nicholas T.; Grooms, Ian; Rieker, Gregory B.; Hamlington, Peter E.

doi:10.1007/s00348-020-03122-2

Cited by 3 publications

(1 citation statement)

References 83 publications

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“…Approximate Bayesian computation (ABC) stands out as a widely-used likelihoodfree estimation method, particularly in biological sciences and various fields, to fit complex models in simulation studies (Toni et al 2009;Aryal and Jones 2020;Cisewski-Kehe et al 2019;Corander et al 2017;Christopher et al 2021;Csilléry et al 2012;McKinley et al 2009;Wilkinson and Tavaré 2009). ABC methods find their application in modelling scenarios where the likelihood function of a model (Cox 2006) is either mathematically intractable or computationally expensive to evaluate.…”

Section: Background Of the Studymentioning

confidence: 99%

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation

Twumasi,

Cable,

Pepelyshev

2024

Bull Math Biol

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The development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid $$\tau $$ τ -leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics.

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Section: Background Of the Studymentioning

confidence: 99%

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation

Twumasi,

Cable,

Pepelyshev

2024

Bull Math Biol

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Physics-trained neural network for sparse-view volumetric laser absorption imaging of species and temperature in reacting flows

et al. 2021

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A deep learning method for laser absorption tomography was developed to effectively integrate physical priors related to flow-field thermochemistry and transport. Mid-fidelity reacting flow simulations were coupled with a forward molecular absorption model to train a deep neural network that performs the tomographic inversion of laser absorption images to predict temperature and species fields in flames. The method was evaluated through numerical simulation and experimental testing in benchtop laminar flames. The target flow-fields involved a spatially-convolved laminar ethylene-flame doublet, backlit with tunable radiation from a quantum cascade laser near 4.85 µm probing rovibrational absorption transitions of carbon monoxide. 2D images were collected at 11 different projection angles, yielding an aggregate of 50,688 unique lines of sight capturing the scene with a pixel resolution of approximately 70 µm. A convolutional neural network was introduced to efficiently generate temperature and species profiles and trained with a large dataset of large-eddy simulations of laminar flames at variable conditions. The learning-based approach to the inversion problem was found to more accurately predict species and temperature fields of the flame with fewer projection angles, reduce convergence time, and expand the field domain relative to classical linear tomography.

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Parameter Estimation for Reynolds-Averaged Navier–Stokes Models Using Approximate Bayesian Computation

2021

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Flow parameter estimation using laser absorption spectroscopy and approximate Bayesian computation

Cited by 3 publications

References 83 publications

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation

Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation

Physics-trained neural network for sparse-view volumetric laser absorption imaging of species and temperature in reacting flows

Parameter Estimation for Reynolds-Averaged Navier–Stokes Models Using Approximate Bayesian Computation

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