Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
工具之后,问题仍在 ——从有限元软件的冲击到人工智能时代人的成长. (Chinese) |
| Alternate Title: |
After Tools, Problems Remain: From the Impact of Finite Element Software to Human Growth in the Age of Artificial Intelligence. (English) |
| Συγγραφείς: |
卢天健 |
| Πηγή: |
Applied Mathematics & Mechanics (1000-0887); Jun2026, Vol. 47 Issue 6, p687-698, 12p |
| Θεματικοί όροι: |
Artificial intelligence, Mechanics (Physics), Simulation software, Academia, Human growth, Digital technology |
| Abstract (English): |
Previous editorials of this journal have discussed academic lineage, intelligent tools, research criteria, and disciplinary direction. After these discussions, a more fundamental question remains; when tools become increasingly powerful, can human researchers still preserve their ability to ask questions, make judgments, and grow? From the perspective of mechanics, artificial intelligence is not the first powerful tool revolution. The finite element method, computers, and engineering software once liberated mechanicians from tedious mathematical calculations and enabled complex structures, boundaries, and loading conditions to become computationally tractable. Yet they also reshaped the identity, confidence, and institutional space of mechanics. In many universities and engineering schools, independent mechanics, applied mechanics, or engineering mechanics programs were compressed, merged, or marginalized during broader disciplinary reorganizations. The unease and sense of displacement brought by Al today are therefore not entirely new; they echo, and in some ways deepen, the impact once brought by computational tools and finite element software. Al goes beyond numerical solution. It enters literature retrieval, writing, coding, diagram generation, scheme construction, and even the appearance of problem formulation. In the age of powerful tools, what is truly scarce is not the ability to call tools more skillfully, but the ability to see the object, explain the mechanism, formulate the question, and take responsibility. Being able to build a model, generate a mesh, and obtain a contour plot does not mean that one has understood the object, the boundary conditions, the underlying mechanisms, or the responsibility of engineering judgment. Generating answers, texts, or schemes is not the same as posing questions, forming judgment, or creating knowledge. Generating a new appearance is not the same as opening a zero-to-one original entry point; such an entry point can only be recognized, undertaken, and advanced by human researchers through real objects, real contradictions, real boundaries, and real responsibility. In a recent MechanoEngineering Editorial, Gao Huajian emphasized that mechanism must precede performance and that AI may accelerate search, but mechanics must judge what is real. This statement captures a central issue in the age of powerful tools: tools may generate candidate outcomes faster, but they cannot automatically provide physical admissibility; algorithms may expand the search space, but they cannot replace conservation laws, constitutive structures, boundary conditions, stability criteria, failure envelopes, or experimental validation. Speed without mechanism is not progress; it is only faster motion. AI is not a matter of whether it should be used, but of how it is used, by whom, to what extent, and with what responsibility. True education is to help students pass through the fear and illusion created by powerful tools and bring them above tools. After tools, problems remain; after problems, human growth begins. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
本刊此前几篇按语已分别讨论了学术根脉、智能工具、研究尺度与学科方向, 这些讨论之后, 还需要追问一个更靠前的问题: 当工具越来越强, 人是否还能守住问题、判断和成长? 从力学史看, 人工智能并不是第一次强工具革命, 有限元方法、计算机和工程软件曾经把力学人从大量繁琐数学求解中解放出来, 使复杂结构、复杂边界和复杂载荷进入可计算状态; 但它们也曾反过来冲击力学人的位置感、学科自信和建制空间, 不少独立力学、应用力学或工程力学方向在工程学科重组中被压缩、合并或边缘化, 其影响至今仍未完全消散, 今天 AI 带来的迷茫、无力和恐慌, 与当年计算工具和有限元软件带来的冲击有相通之处, 甚至更深, 因为 AI 不只进入求解环节, 还进入检索、写作、表达、代码、图表、方案和问题外观的生成环节。 强工具时代最稀缺的, 不是更熟练地调用, 而是能不能看清对象、讲清机理、提出问题并承担责任, 会建模、会划网格、会看云图, 并不等于看清对象、理解边界、解释机理和承担责任; 生成答案、生成文本、生成方案, 也不等于提出问题、形成判断和完成创造, 生成新外观也不等于打开从 0 到 1 的新入口; 真正的原创入口, 只能从真实对象、真实矛盾、真实边界和真实责任中被人辨认、承担和推进, 近期 MechanoEngineering Editorial 中提出 "机制必须先于性能", 并强调 AI 可以加速搜索, 但力学必须判断什么是真实, 这一判断正好提醒我们; 工具可以更快生成候选结果, 却不能自动给出物理可采纳性; 算法可以扩大搜索空间, 却不能替代守恒约束、本构结构、边界条件、稳定性判据、 失效包络和实验验证, 速度如果不受机制约束, 就不是进步, 只是更快的运动。AI 不是要不要用的问题, 而是怎样用、由谁用、用到哪里、谁来负责的问题, 大学教师不能因 AI 风险而反 AI, 也不能放任学生成为工具的附属物; 真正的教书育人, 是帮助学生穿过强工具带来的恐慌和幻觉, 把他们带到工具之上. 工具之后, 问题仍在; 问题之后, 是人的成长。 [ABSTRACT FROM AUTHOR] |
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