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This paper presents a novel mesh-free approach to the hyperbolic mean curvature flow (HMCF) using physics-informed neural networks (PINNs), which allows for efficient numerical solutions of partial differential equations without the need for discretization. The authors demonstrate the effectiveness of their method through various numerical simulations involving closed curves and surfaces, addressing challenges such as mesh distortion and computational burden associated with traditional numerical methods. Th

Author

hovindo

Language

EN