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Solving real-world optimization tasks using physics-informed neural computing - Scientific Reports

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Solving real-world optimization tasks using physics-informed neural computing - Scientific Reports

Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development. Recently, machine learning applications, including deep reinforcement learning (RL) and genetic algorithms (GA), have emerged in these real-world optimization tasks. We introduce a new machine learning-based optimization scheme that incorporates physics with the operational objectives. This physics-informed neural network (PINN) could

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Solving real-world optimization tasks using physics-informed neural computing - Scientific Reports

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Solving real-world optimization tasks using physics-informed neural computing - Scientific Reports

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