arXiv:2411.00011
공식 코드 · Papers with Code
Solving the 2D Advection-Diffusion Equation using Fixed-Depth Symbolic Regression and Symbolic Differentiation without Expression Trees
원문 초록
This paper presents a novel method for solving the 2D advection-diffusion equation using fixed-depth symbolic regression and symbolic differentiation without expression trees. The method is applied to two cases with distinct initial and boundary conditions, demonstrating its accuracy and ability to find approximate solutions efficiently. This framework offers a promising, scalable solution for finding approximate solutions to differential equations, with the potential for future improvements in computational performance and applicability to more complex systems involving vector-valued objectives.
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Discovering the underlying analytic structure within Standard Model constants using artificial intelligence
Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics
syren-baryon: Analytic emulators for the impact of baryons on the matter power spectrum
Improving Memory Efficiency for Training KANs via Meta Learning
Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
Automated Modeling Method for Pathloss Model Discovery
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