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How to solve computational science problems with AI: PINNs


ysics-Informed Neural Networks (PINNs) Introduction # In today’s world, numerous challenges exist, particularly in computational science. This post focuses on solving scientific problems through simulations, such as computational physics.

By addressing these issues, we can better understand the requirements for effective PDE solutions and the potential for innovative approaches like Physics-Informed Neural Networks (PINNs) to overcome these challenges. AI models offer a powerful method for solving complex partial differential equations (PDEs) by integrating physical laws into neural network training. As computational science advances, AI models are set to become invaluable tools for researchers and engineers, enabling the solution of previously intractable problems across various fields.

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