Multicore Parallel Tempering Bayeslands for Basin and Landscape Evolution

Chandra, Rohitash; Müller, R. Dietmar; Azam, Danial; Deo, Ratneel; Butterworth, N. P.; Salles, Tristan; Cripps, Sally

doi:10.1029/2019gc008465

Cited by 18 publications

(39 citation statements)

References 89 publications

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“…In our case, convergence is defined by a predefined number of samples or until the likelihood function has reached a specific value. Convergence essentially means that the posterior distribution of the given parameters generate Badlands model outputs that resemble ground-truth data [1].…”

Section: Bayesian Inference Via Parallel Temperingmentioning

confidence: 99%

“…In order to create test problems for Badlands, a set of climate and geological parameters defined by θ needs to be predefined to determine landscape evolution over a given timescale T . The final (ground-truth) topography at time T and expected sediment deposits at selected intervals in time are used to evaluate the quality of proposals during sampling in Bayeslands [1]. Bayeslands features parallel tempering for the estimation of free parameters and uncertainty quantification in model outputs for landscape simulation [1].…”

Section: Badlands and Bayeslandsmentioning

confidence: 99%

“…Hence, a deterministic geophysical forward model can be seen as a probabilistic model via Bayesian inference which provides a rigorous approach to uncertainty quantification as opposed to optimization methods. The approach is also known as Bayesian inversion which has been used for landscape evolution [1,6], geological reef evolution models [7] and other geo-scientific models [4,8]. A Markov Chain Monte Carlo method (MCMC) can be used to implement Bayesian inference for estimation and uncertainty quantification of free parameters [9,10,11,5].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous work, parallel tempering Bayeslands was presented as a framework for parameter estimation and uncertainty quantification for Badlands, a landscape and basin evolution evolution software [1]. Parallel tempering Bayeslands features high performances parallel computing to enhance the efficiency of estimating free parameters of a Badlands model.…”

Section: Introductionmentioning

confidence: 99%

“…Parallel tempering Bayeslands features high performances parallel computing to enhance the efficiency of estimating free parameters of a Badlands model. Although parallel computing is used, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated [1]. In large scale landscape evolution problems, a single model can take hours to days.…”

Section: Introductionmentioning

confidence: 99%

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Surrogate-assisted Bayesian inversion for landscape and basin evolution models

Chandra

Azam

Kapoor

et al. 2019

Preprint

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The complex and computationally expensive features of the forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In our previous work, parallel tempering Bayeslands was developed as a framework for parameter estimation and uncertainty quantification for the landscape and basin evolution modelling software Badlands [1]. Parallel tempering Bayeslands features high-performance computing with dozens of processing cores running in parallel to enhance computational efficiency. Although parallel computing is used, the procedure remains computationally challenging since thousands of samples need to be drawn and evaluated. In large-scale landscape and basin evolution problems, a single model evaluation can take from several minutes to hours, and in certain cases, even days. Surrogate-assisted optimization has been with successfully applied to a number of engineering problems [2,3]. This motivates its use in optimisation and inference methods suited for complex models in geology and geophysics. Surrogates can speed up parallel tempering Bayeslands by developing computationally inexpensive surrogates to mimic expensive models. In this paper, we present an application of surrogate-assisted parallel tempering where that surrogate mimics a landscape evolution model including erosion, sediment transport and deposition, by estimating the likelihood function that is given by the model. We employ a machine learning model as a surrogate that learns from the samples generated by the parallel tempering algorithm and the likelihood from the model. The entire framework is developed in a parallel computing infrastructure to take advantage of parallelization. The results show that the proposed methodology is effective in lowering the overall computational cost significantly while retaining the quality of solutions.

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Section: Bayesian Inference Via Parallel Temperingmentioning

confidence: 99%

Section: Badlands and Bayeslandsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surrogate-assisted Bayesian inversion for landscape and basin evolution models

Chandra

Azam

Kapoor

et al. 2019

Preprint

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Surrogate-assisted distributed swarm optimisation for computationally expensive geoscientific models

Chandra¹,

Vardhan²

2023

Comput Geosci

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Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggle. We can incorporate efficient strategies such as surrogate-assisted optimisation to address the challenges; however, implementing inter-process communication for surrogate-based model training is difficult. In this paper, we implement surrogate-based estimation of fitness evaluation in distributed swarm optimisation over a parallel computing architecture. We first test the framework on a set of benchmark optimisation problems and then apply to a geoscientifc model that features landscape evolution model. Our results demonstrate very promising results for benchmark functions and the Badlands landscape evolution model. We obtain a reduction in computationally time while retaining optimisation solution accuracy through the use of surrogates in a parallel computing environment. The major contribution of the paper is in the application of surrogate-based optimisation for geoscientific models which can in the future help in better understanding of paleoclimate and geomorphology.

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