This contribution examines the capabilities of the Python ecosystem to solve nonlinear energy minimization problems, with a particular focus on transitioning from traditional MATLAB methods to Python's advanced computational tools, such as automatic differentiation. We demonstrate Python's streamlined approach to minimizing nonlinear energies by analyzing three problem benchmarks - the p-Laplacian, the Ginzburg-Landau model, and the Neo-Hookean hyperelasticity. This approach merely requires the provision of the energy functional itself, making it a simple and efficient way to solve this category of problems. The results show that the implementation is about ten times faster than the MATLAB implementation for large-scale problems. Our findings highlight Python's efficiency and ease of use in scientific computing, establishing it as a preferable choice for implementing sophisticated mathematical models and accelerating the development of numerical simulations.

翻译：本文探讨了Python生态系统在求解非线性能量最小化问题方面的能力，特别关注从传统MATLAB方法向Python先进计算工具（如自动微分）的过渡。通过分析三个基准问题——p-Laplacian、Ginzburg-Landau模型和Neo-Hookean超弹性模型，我们展示了Python在最小化非线性能量方面的简化流程。该方法仅需提供能量泛函本身，为求解此类问题提供了一种简洁高效的途径。结果表明，在大规模问题上该实现的运行速度约为MATLAB实现的十倍。我们的发现凸显了Python在科学计算中的高效性和易用性，确立了其在实现复杂数学模型和加速数值模拟开发方面的优选地位。