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Params: post, 638918, a:28:{i:0;s:183:"根据杜克大学的研究人员的说法，将已知的物理学融入机器学习算法可以帮助神秘的“黑匣子”获得更高的透明度和对材料特性的洞察力。";i:1;a:4:{s:3:"tag";s:1:"b";s:6:"option";N;s:8:"original";a:2:{i:0;s:3:"[B]";i:1;s:4:"[/B]";}s:8:"children";a:1:{i:0;s:162:"研究人员使用了一种复杂的机器学习算法来识别一类被称为超材料的工程材料的特性，并预测它们如何与电磁场相互作用。";}}i:2;s:2:"
";i:3;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:255:"该算法基本上被迫展示其工作，因为它首先必须考虑超材料的已知物理限制。该方法不仅使算法能够高精度地预测超材料的特性，而且与早期的方法相比，它做得更快，并且具有更多的洞察力。";}}i:4;s:2:"
";i:5;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:3:{i:0;s:41:"该结果于2022 年5月13日发表在《";i:1;a:4:{s:3:"tag";s:3:"url";s:6:"option";s:58:"https://onlinelibrary.wiley.com/doi/10.1002/adom.202200097";s:8:"original";a:2:{i:0;s:66:"[URL='https://onlinelibrary.wiley.com/doi/10.1002/adom.202200097']";i:1;s:6:"[/URL]";}s:8:"children";a:1:{i:0;s:18:"先进光学材料";}}i:2;s:15:"》杂志上。";}}i:6;s:2:"
";i:7;a:4:{s:3:"tag";s:6:"center";s:6:"option";N;s:8:"original";a:2:{i:0;s:8:"[CENTER]";i:1;s:9:"[/CENTER]";}s:8:"children";a:1:{i:0;a:4:{s:3:"tag";s:3:"img";s:6:"option";N;s:8:"original";a:2:{i:0;s:5:"[IMG]";i:1;s:6:"[/IMG]";}s:8:"children";a:1:{i:0;s:90:"https://rdimg.com/proxy/https://static.cnbetacdn.com/article/2022/0617/37ff813b4e8c7e7.png";}}}}i:8;s:2:"
";i:9;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:362:"杜克大学电气和计算机工程教授Willie Padilla说：“通过将已知物理学直接纳入机器学习，该算法可以在更少的训练数据和更短的时间内找到解决方案。虽然这项研究主要是展示该方法可以重建已知解决方案，但它也揭示了一些以前没人知道的非金属超材料的内部工作原理。”";}}i:10;s:2:"
";i:11;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:249:"超材料是由许多单独的工程特征组成的合成材料，它们通过它们的结构而不是它们的化学共同产生自然界中不存在的特性。在这种情况下，超材料由类似于乐高底板的大型硅圆柱网格组成。";}}i:12;s:2:"
";i:13;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:345:"根据圆柱体的尺寸和间距，超材料以各种方式与电磁波相互作用，例如吸收、发射或偏转特定波长。在新论文中，研究人员试图建立一种称为神经网络的机器学习模型，以发现单圆柱体的一系列高度和宽度如何影响这些相互作用。但他们也希望它的答案有意义。";}}i:14;s:2:"
";i:15;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:347:"“神经网络试图在数据中找到模式，但有时他们发现的模式不符合物理定律，这使得它创建的模型不可靠，”杜克大学电气和计算机工程助理研究教授 Jordan Malof 说。“通过强制神经网络遵守物理定律，我们阻止了它找到可能适合数据但实际上并不真实的关系。”";}}i:16;s:2:"
";i:17;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:333:"研究小组强加于神经网络的物理学称为洛伦兹模型——一组描述材料固有特性如何与电磁场共振的方程。该模型不是直接跳到预测气缸的响应，而是必须学会预测洛伦兹参数，然后用于计算气缸的响应。然而，加入这个额外的步骤说起来容易做起来难。";}}i:18;s:2:"
";i:19;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:237:"Padilla 实验室的博士后研究员 Omar Khatib 说：“当你让神经网络更具可解释性时，这在某种意义上是我们在这里所做的，微调可能更具挑战性。我们肯定很难优化训练来学习模式。”";}}i:20;s:2:"
";i:21;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:306:"然而，一旦模型开始工作，它就被证明比该小组为相同任务创建的先前神经网络更有效。特别是，该小组发现这种方法可以显著减少模型确定超材料特性所需的参数数量。他们还发现，这种基于物理学的人工智能方法能够自行发现。";}}i:22;s:2:"
";i:23;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:303:"当电磁波穿过一个物体时，它在其旅程开始时与它的交互方式不一定与它在其结束时完全相同。这种现象被称为空间色散。由于研究人员必须调整空间色散参数以使模型准确工作，他们发现了他们以前不知道的过程物理学的见解。";}}i:24;s:2:"
";i:25;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:127:"“现在我们已经证明这是可以做到的，我们希望将这种方法应用于物理未知的系统，”Padilla说。";}}i:26;s:2:"
";i:27;a:4:{s:3:"tag";s:4:"left";s:6:"option";N;s:8:"original";a:2:{i:0;s:6:"[LEFT]";i:1;s:7:"[/LEFT]";}s:8:"children";a:1:{i:0;s:267:"“很多人都在使用神经网络来预测材料特性，但从模拟中获得足够的训练数据是一个巨大的痛苦，”Malof 补充道。“这项工作还展示了一条创建不需要太多数据的模型的途径，这在所有方面都很有用。”";}}}, 1621686945, 1714626221
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