History Matching with Generative Adversarial Networks

Razak, Syamil Mohd; Jafarpour, Behnam

doi:10.3997/2214-4609.202035217

Cited by 6 publications

(2 citation statements)

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“…Then a GAN is trained on these realisations in order to generate calibrated models. [51] used a similar approach of clustering the prior models; however, they also apply a CGAN to label production responses of each model. [11] compared different deep generative networks formulations, including GANs and VAE, integrated with a Kalman filter-based method for proper data assimilation of facies models in reservoir simulations.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology

Silva

Heaney

et al. 2022

J Sci Comput

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We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

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Section: Introductionmentioning

confidence: 99%

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology

Silva

Heaney

et al. 2022

J Sci Comput

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show abstract

“…GANs have been used to predict spatio-temporal solutions for super-resolution fluid flow [38], carbon capture [41], incoming waves from Hokkaido tsunami [11], and the spread of COVID-19 [29]. GANs have also been used in the processes of data assimilation and uncertainty quantification to generate conditional models parameters [21,16,28,10]. Even though in these works they still need to simulate the high-fidelity numerical model to predict forward in time.…”

Section: Introductionmentioning

confidence: 99%

GAN for time series prediction, data assimilation and uncertainty quantification

Silva¹,

Heaney²,

Pain³

2021

Preprint

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We propose a new method in which a generative adversarial network (GAN) is used to quantify the uncertainty of forward simulations in the presence of observed data. Previously, a method has been developed which enables GANs to make time series predictions and data assimilation by training a GAN with unconditional simulations of a high-fidelity numerical model. After training, the GAN can be used to predict the evolution of the spatial distribution of the simulation states and observed data is assimilated. In this paper, we describe the process required in order to quantify uncertainty, during which no additional simulations of the high-fidelity numerical model are required. These methods take advantage of the adjoint-like capabilities of generative models and the ability to simulate forwards and backwards in time. Set within a reduced-order model framework for efficiency, we apply these methods to a compartmental model in epidemiology to predict the spread of COVID-19 in an idealised town. The results show that the proposed method can efficiently quantify uncertainty in the presence of measurements using only unconditional simulations of the high-fidelity numerical model.Preprint. Under review.

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Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data

2021

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History Matching with Generative Adversarial Networks

Cited by 6 publications

References 0 publications

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology

GAN for time series prediction, data assimilation and uncertainty quantification

Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data

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