Physics-Informed Localized Learning for Advection-Diffusion-Reaction
Systems
- URL: http://arxiv.org/abs/2305.03774v2
- Date: Fri, 30 Jun 2023 18:35:45 GMT
- Title: Physics-Informed Localized Learning for Advection-Diffusion-Reaction
Systems
- Authors: Surya T. Sathujoda and Soham M. Sheth
- Abstract summary: Carbon Capture and Sequestration initiatives and green energy solutions, such as geothermal, have thrust new demands upon subsurface fluid simulators.
We propose a novel physics-informed and boundary condition-aware Localized Learning method which extends the Embed-to-Control (E2C) and Embed-to-Control and Observe (E2CO) models.
We show that our model is able to predict future states of the system, for a given set of controls, to a great deal of accuracy with only a fraction of the available information.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The global push to advance Carbon Capture and Sequestration initiatives and
green energy solutions, such as geothermal, have thrust new demands upon the
current state-of-the-art subsurface fluid simulators. The requirement to be
able to simulate a large order of reservoir states simultaneously, in a short
period of time, has opened the door of opportunity for the application of
machine learning techniques for surrogate modelling. We propose a novel
physics-informed and boundary condition-aware Localized Learning method which
extends the Embed-to-Control (E2C) and Embed-to-Control and Observe (E2CO)
models to learn local representations of global state variables in an
Advection-Diffusion Reaction system. Trained on reservoir simulation data, we
show that our model is able to predict future states of the system, for a given
set of controls, to a great deal of accuracy with only a fraction of the
available information. It hence reduces training times significantly compared
to the original E2C and E2CO models, lending to its benefit in application to
optimal control problems.
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