What are the current state-of-the-art techniques used in applying physics-informed machine learning to the oil industry?
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Physics-informed machine learning techniques are being applied in the oil industry to address complex problems. These techniques combine data-driven approaches with physics-based constraints to improve prediction accuracy and computational efficiency. Four embedding mechanisms have been identified for introducing physical information into machine learning models: input databased embedding, model architecture-based embedding, loss function-based embedding, and model optimization-based embedding. These dual-driven intelligent models adhere to physical laws while achieving higher prediction accuracy and faster convergence. They offer a comprehensive and efficient approach to solving petroleum engineering problems.
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24 Jan 2023 1 Citations | The paper does not explicitly mention the current state-of-the-art techniques used in applying physics-informed machine learning to the oil industry. |
| The paper discusses four embedding mechanisms for introducing physical information into machine learning models: input data-based, model architecture-based, loss function-based, and model optimization-based embedding mechanisms. | |
| The provided paper does not mention the current state-of-the-art techniques used in applying physics-informed machine learning to the oil industry. | |
| The paper does not mention the current state-of-the-art techniques used in applying physics-informed machine learning to the oil industry. | |
| The provided paper does not specifically mention the current state-of-the-art techniques used in applying physics-informed machine learning to the oil industry. |
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