谷歌与诺奖的交集，让基础研究与应用研究的边界越发模糊，这为人们提供了一个新评价标准，也就是 说，基础研究的商业价值并非不可度量，科学的猜想与反驳、商业的价值发现，二者之间不再"违和"。 其实，谷歌连续获诺奖只是以一种更富话题性和冲击力的形式表达出了基础研究与应用研究边界的日渐 模糊，更早将两者边界打破的是马斯克，其基于物理学第一性原理的创业和企业运营信念，早已将基础 研究与应用研究、学术价值与商业价值有效交融，成为马斯克旗下公司开创商业价值的原力。 学术殿堂与商战从未如此靠近。 一年一度的诺奖评选产生的颠覆性已远超过学术圈，正在更广阔的领域展开一场解构与建构式行动。继 去年谷歌DeepMind首席执行官戴米斯?哈萨比斯（Demis Hassabis）和高级研究科学家约翰·M.詹珀 （John M. Jumper）荣获诺贝尔化学奖后，2025年诺贝尔物理学奖授予三位在量子力学领域做出开创性 贡献的物理学家，包括谷歌量子AI实验室的现任硬件首席科学家米歇尔·德沃雷特（Michel Devoret）， 及曾领导其硬件团队多年的约翰·马丁尼斯（John Martinis）。 谷歌连续两年在不同前沿领域获得诺奖，表明诺 ...

Core Insights - Google DeepMind collaborates with researchers from NYU, Stanford, and Brown University to discover new unstable singularities in fluid equations using machine learning and a high-precision Gauss-Newton optimizer [1][2] - The research aims to tackle the Navier-Stokes equations, one of the Millennium Prize Problems, with a potential reward of $1 million for a solution [1][2] - The project, named "Navier-Stokes Initiative," has been ongoing for three years and involves a 20-member team working under high confidentiality [1][2] Research Findings - The team has systematically identified new unstable singularities in three different fluid equations and established a simple empirical asymptotic formula linking blow-up rates to instability orders [1][2] - The method achieved significant precision improvements over existing works, with results approaching machine limits, constrained only by GPU rounding errors [2][5] Methodology - The research employs a two-phase structure for discovering and analyzing unstable singularities, focusing on high-precision solutions [5][6] - A candidate solution is used to search for self-similar blow-up solutions, followed by iterative optimization of the machine learning process [6][9] - The use of Physics-Informed Neural Networks (PINN) combined with the Gauss-Newton optimizer and multi-stage training strategies enhances the accuracy of the solutions [11][13] Training Enhancements - Key improvements in the training process include the introduction of the Gauss-Newton optimizer, which outperforms standard gradient optimizers in producing high-quality solutions [13][15] - Multi-stage training involves training one network to approximate solutions and a second network to correct errors, significantly enhancing precision [13][15] Results and Implications - The model's accuracy has reached new levels, with maximum errors comparable to predicting the Earth's diameter within a few centimeters [15] - The findings provide a new research paradigm for exploring the complexities of nonlinear partial differential equations (PDEs) and could pave the way for solving long-standing mathematical physics challenges [2][5]