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The document presents a conservative physics-informed neural network (cPINN) designed for nonlinear conservation laws on discrete domains, enhancing the accuracy and efficiency of solving both forward and inverse problems. It employs domain decomposition, allowing separate neural networks in sub-domains to enforce flux continuity and reduce error propagation while facilitating parallel computation. The methodology is validated through various numerical experiments, demonstrating its effectiveness in handlin

Author

Mathieu Salz

Language

EN