Use of Multifidelity Training Data and Transfer Learning for Efficient
Construction of Subsurface Flow Surrogate Models
- URL: http://arxiv.org/abs/2204.11138v1
- Date: Sat, 23 Apr 2022 20:09:49 GMT
- Title: Use of Multifidelity Training Data and Transfer Learning for Efficient
Construction of Subsurface Flow Surrogate Models
- Authors: Su Jiang, Louis J. Durlofsky
- Abstract summary: To construct data-driven surrogate models, several thousand high-fidelity simulation runs may be required to provide training samples.
We present a framework where most of the training simulations are performed on coarsened geomodels.
The network provides results that are significantly more accurate than the low-fidelity simulations used for most of the training.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Data assimilation presents computational challenges because many
high-fidelity models must be simulated. Various deep-learning-based surrogate
modeling techniques have been developed to reduce the simulation costs
associated with these applications. However, to construct data-driven surrogate
models, several thousand high-fidelity simulation runs may be required to
provide training samples, and these computations can make training
prohibitively expensive. To address this issue, in this work we present a
framework where most of the training simulations are performed on coarsened
geomodels. These models are constructed using a flow-based upscaling method.
The framework entails the use of a transfer-learning procedure, incorporated
within an existing recurrent residual U-Net architecture, in which network
training is accomplished in three steps. In the first step. where the bulk of
the training is performed, only low-fidelity simulation results are used. The
second and third steps, in which the output layer is trained and the overall
network is fine-tuned, require a relatively small number of high-fidelity
simulations. Here we use 2500 low-fidelity runs and 200 high-fidelity runs,
which leads to about a 90% reduction in training simulation costs. The method
is applied for two-phase subsurface flow in 3D channelized systems, with flow
driven by wells. The surrogate model trained with multifidelity data is shown
to be nearly as accurate as a reference surrogate trained with only
high-fidelity data in predicting dynamic pressure and saturation fields in new
geomodels. Importantly, the network provides results that are significantly
more accurate than the low-fidelity simulations used for most of the training.
The multifidelity surrogate is also applied for history matching using an
ensemble-based procedure, where accuracy relative to reference results is again
demonstrated.
Related papers
- Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models [0.34592277400656235]
Multi-fidelity machine learning methods integrate scarce, resource-intensive high-fidelity data with abundant but less accurate low-fidelity data.
We propose a practical multi-fidelity strategy for problems spanning low- and high-dimensional domains.
arXiv Detail & Related papers (2024-07-21T10:40:50Z) - Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
We present SIM-FSVGD for learning robot dynamics from data.
We use low-fidelity physical priors to regularize the training of neural network models.
We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
arXiv Detail & Related papers (2024-03-25T11:29:32Z) - Multifidelity linear regression for scientific machine learning from scarce data [0.0]
We propose a new multifidelity training approach for scientific machine learning via linear regression.
We provide bias and variance analysis of our new estimators that guarantee the approach's accuracy and improved robustness to scarce high-fidelity data.
arXiv Detail & Related papers (2024-03-13T15:40:17Z) - In Situ Framework for Coupling Simulation and Machine Learning with
Application to CFD [51.04126395480625]
Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations.
As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks.
This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
arXiv Detail & Related papers (2023-06-22T14:07:54Z) - A Physics-informed Diffusion Model for High-fidelity Flow Field
Reconstruction [0.0]
We propose a diffusion model which only uses high-fidelity data at training.
With different configurations, our model is able to reconstruct high-fidelity data from either a regular low-fidelity sample or a sparsely measured sample.
Our model can produce accurate reconstruction results for 2d turbulent flows based on different input sources without retraining.
arXiv Detail & Related papers (2022-11-26T23:14:18Z) - Data efficient surrogate modeling for engineering design: Ensemble-free
batch mode deep active learning for regression [0.6021787236982659]
We propose a simple and scalable approach for active learning that works in a student-teacher manner to train a surrogate model.
By using this proposed approach, we are able to achieve the same level of surrogate accuracy as the other baselines like DBAL and Monte Carlo sampling.
arXiv Detail & Related papers (2022-11-16T02:31:57Z) - Continual learning autoencoder training for a particle-in-cell
simulation via streaming [52.77024349608834]
upcoming exascale era will provide a new generation of physics simulations with high resolution.
These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible.
This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
arXiv Detail & Related papers (2022-11-09T09:55:14Z) - Simulation-Based Parallel Training [55.41644538483948]
We present our ongoing work to design a training framework that alleviates those bottlenecks.
It generates data in parallel with the training process.
We present a strategy to mitigate this bias with a memory buffer.
arXiv Detail & Related papers (2022-11-08T09:31:25Z) - Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs.
We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling.
We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
arXiv Detail & Related papers (2022-06-10T04:54:13Z) - Improving Non-autoregressive Generation with Mixup Training [51.61038444990301]
We present a non-autoregressive generation model based on pre-trained transformer models.
We propose a simple and effective iterative training method called MIx Source and pseudo Target.
Our experiments on three generation benchmarks including question generation, summarization and paraphrase generation, show that the proposed framework achieves the new state-of-the-art results.
arXiv Detail & Related papers (2021-10-21T13:04:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.