波光粼粼的水面上，一艘艘流线型船模正灵巧穿梭，红外感应的"眼睛"与风帆的"翅 膀"共同演绎着水面上的"速度与智慧"。9日，第十四届全国海洋航行器设计与制作大赛在武 汉理工大学拉开帷幕，来自全国近400家单位的3345支队伍、1.6万名学生参赛。 编辑：赖俊 "嗖——"一艘银灰色船模破水而出，精准穿过标有红外感应装置的门架，门架上的指示 灯应声亮起。"它全靠自己'看路'！"现场工作人员指着船模顶部的接收器介绍，无需人工操 控，船上的智能系统能实时感知亮灯的门架位置，自动调整船舵转角，像有了自主意识般规 划路径。而船身的设计更是暗藏玄机：3D打印的流线型外壳光滑如镜。"越符合流体力学， 水阻越小，速度就越快。"一位参赛选手边调试船模边解释。 距此百米外的武汉理工大学船舶操纵水池内，参赛作品分为水面、水下两组，选手们带 着精心打磨的"利器"在此一较高下。水面上，各式航行器破浪驰骋；水底下，潜航设备悄然 穿梭，生动展现着海洋装备的多样技能与前沿科技。 据了解，全国海洋航行器设计与制作大赛秉承"崇尚科学、实践求知、锐意创新、面向 海洋、服务国防"的宗旨，发展成为国内船舶与海洋工程领域顶级赛事。本届大赛还融入了 第二届船 ...

2021年，一条"92岁周恒院士骑自行车去办公室做科研"的短视频走红网络。天津大学的校园里，这位年 过九旬的院士周恒脚踏自行车，笑着挥手奔赴实验室的背影，让无数人动容。 如今，自行车停放在墙角，带着睿智温润、实事求是的一身风骨，骑车的人走了。 "在哪里能起作用，文章就发在哪里"，这是他与团队的信条。 "遇到一个好导师会让你走上正确的科研之路。正确不仅是方向的正确，更是价值观的正确。"天津大学 机械学院青年教师陈杰2017年加入周恒团队，在她眼中，周恒对她的影响不仅在学术选择上，还在看待 科研的态度上。 周恒生前照片。（天津大学供图） 8月1日，我国著名流体力学专家、教育家，中国科学院院士，天津大学机械学院名誉院长周恒教授，在 天津离世，享年96岁。 周恒生前照片。（天津大学供图） 1929年，周恒出生于上海。彼时的中国风雨飘摇，时局困顿，乱世中他却未曾中断求知的脚步。 抗战胜利后，没接触过物理的他，靠着自学考取了天津大学的前身——北洋大学。之后，又以全班第一 的成绩毕业留校任教。23岁的周恒第一次站在讲台上，开讲的第一门课就是理论力学。从此，他毕生都 在这条道路上求索。 从工程配件到航空航天，从空气动力学到稀 ...

来源 ：天津日报、 今晚报、天津大学网站 周恒同志1929年11月20日出生于上海市。1946年考入北洋大学化工系，后转入水利系， 1950年毕业并留校任教至今。1951年加入中国民主同盟。1983年至1993年任天津大学力学 系主任，1984年至1986年任天津大学研究生院副院长，1986年任天津大学研究生院院长。 1993年当选为中国科学院学部委员（院士）。 我国著名流体力学专家、教育家，中国科学院院士，第八、第九届全国政协委员，中国民主同 盟盟员，天津大学机械学院名誉院长周恒教授，因病医治无效，于2025年8月1日8时54分在 天津逝世，享年96岁。 周恒同志长期致力于流体力学、应用数学等领域的研究与教学工作，将毕生精力奉献于祖国的 科学事业与高等教育事业。他在流体力学稳定性理论、湍流研究等领域取得了一系列开创性成 果，为我国力学学科的发展作出了卓越贡献。 周恒同志为我国力学科学和教育事业奋斗一生。他矢志报国的赤子情怀、追求真理的科学精 神、诲人不倦的师者风范，将永远为我们所铭记和景仰！ 刚刚预警！多地将 现大暴雨 ...

Group 1 - The dialogue between Zhang Chaoyang and David Tong covers the evolution of physics from classical mechanics to quantum mechanics and field theory, emphasizing the importance of mathematical rigor in understanding physical laws [1][2][3] - The discussion highlights significant milestones in physics, including Newton's laws, Einstein's theories, and the development of quantum mechanics, showcasing how these theories have transformed our understanding of the universe [2][16][19] - The conversation also touches on the role of fluid dynamics in physics, particularly in understanding complex phenomena such as the behavior of quark-gluon plasma and its implications for the universe [8][12][13] Group 2 - The importance of scientific communication and public education is emphasized, with a belief that rigorous mathematics should not be avoided in popular science [35][41] - The potential of AI in assisting physicists is discussed, highlighting its role in solving complex equations and aiding research, while also acknowledging the irreplaceable value of human interaction in education [10][11][38] - The dialogue concludes with reflections on the future of scientific dissemination, suggesting that the next generation of scientists should embrace the challenge of making complex theories accessible without oversimplifying the underlying mathematics [36][40][41]

Core Viewpoint - The article discusses the emerging significance of Flow Matching technology in the field of generative AI, highlighting its connection to fluid dynamics and its potential to enhance model quality and stability [4][5][8]. Group 1: Flow Matching Technology - Flow Matching technology is gaining attention for its ability to address key elements in generative AI, such as quality, stability, and simplicity [5]. - The FLUX model has catalyzed interest in Flow Matching architectures that can handle various input types [6]. - Flow Matching is based on Normalizing Flows (NF), which gradually maps complex probability distributions to simpler ones through a series of reversible transformations [18]. Group 2: Relationship with Fluid Dynamics - The core concept of Flow Matching is derived from fluid dynamics, particularly the continuity equation, which emphasizes that mass cannot be created or destroyed [22][23]. - Flow Matching focuses on the average density of particles in a space, paralleling how it tracks the transition from noise distribution to data distribution [20][25]. - The process involves defining a velocity field that guides the transformation from noise to data, contrasting with traditional methods that start from particle behavior [24][25]. Group 3: Generative Process - The generative process in Flow Matching involves mapping noise to data through interpolation, where the model learns to move samples along a defined path [12][17]. - The method emphasizes the average direction of paths leading to high-probability samples, allowing for effective data generation [30][34]. - Flow Matching can be seen as a special case of diffusion models when Gaussian distribution is used as the interpolation strategy [41]. Group 4: Comparison with Diffusion Models - Flow Matching and diffusion models share similar forward processes, with Flow Matching being a subset of diffusion models [40]. - The training processes of both models exhibit equivalence when Gaussian distributions are employed, although Flow Matching introduces new output parameterization as a velocity field [35][44]. - The design of weight functions in Flow Matching aligns closely with those commonly used in diffusion model literature, impacting the model's performance [45].

Core Insights - Recent research challenges the long-held belief that fish hovering in water is the most energy-efficient resting position, revealing that hovering fish actually burn twice the energy compared to resting states [2][3] Group 1: Fish Physiology and Behavior - All bony fish possess a unique organ called the swim bladder, which allows them to achieve near-perfect neutral buoyancy, similar to how submarines control their buoyancy [2] - The positioning of the swim bladder often does not align with the fish's center of mass, creating a need for constant adjustments to maintain stability while hovering [2] - Different fish species exhibit unique balancing strategies during hovering, with variations in fin positioning and body shape affecting energy efficiency and stability [3] Group 2: Evolutionary Adaptations - The metabolic rate of hovering fish is significantly higher, being twice that of resting fish, indicating a high-energy survival strategy evolved over millions of years [3] - Fish that are adept at high-speed swimming tend to have lower hovering efficiency, while those in complex coral reef environments have evolved rounder bodies for better stability during hovering [3] Group 3: Implications for Technology - The findings from this research could inform the design of underwater robots, suggesting that mimicking fish hovering mechanisms could lead to more energy-efficient and environmentally friendly robotic designs [4]

Core Viewpoint - The article discusses a significant breakthrough in addressing Hilbert's sixth problem, which aims to establish a rigorous mathematical foundation for physics, particularly the transition from microscopic particle dynamics to macroscopic fluid behavior [2][13][35]. Summary by Sections Historical Context - Hilbert's sixth problem, proposed in 1900, questions whether physics can be constructed on a strict mathematical basis similar to Euclidean geometry [1][3]. - The challenge involves linking reversible microscopic laws of motion (Newtonian mechanics) with irreversible macroscopic behaviors (described by the Boltzmann equation) [8][9]. Breakthrough Achieved - Mathematicians Deng Yu, Ma Xiao, and Zaher Hani have made a significant advancement by deriving macroscopic gas behavior from microscopic particle models, bridging the gap between Newtonian mechanics and the Boltzmann equation [10][11][13]. - They provided a rigorous proof of the complete transition from Newtonian mechanics to the Boltzmann equation, addressing the "arrow of time" paradox left by Boltzmann [13][35]. Methodology - The solution involves two main steps: first, deriving the Boltzmann equation from Newton's laws through a "dynamical limit," and second, deriving fluid dynamics equations from the Boltzmann equation through a "fluid dynamical limit" [15][23]. - The team initially focused on wave systems before transitioning to particle systems, recognizing the complexity of particle collisions compared to wave interference [18][21]. Detailed Steps - In the first step, they demonstrated that as the number of hard sphere particles approaches infinity and their diameter approaches zero, the single-particle density can be described by the Boltzmann equation [17]. - In the second step, they showed that as the collision rate in the Boltzmann equation approaches infinity, its solution converges to the local Maxwell distribution, corresponding to macroscopic fluid parameters [24][30]. Implications - This work not only marks a major advancement in solving Hilbert's sixth problem but also provides a rigorous mathematical solution to the long-standing paradox of time irreversibility in physics [35][37]. - The findings establish a complete logical chain from Newtonian mechanics to statistical mechanics and fluid mechanics, enhancing the understanding of physical laws across different scales [31][34].

Core Viewpoint - The article highlights the journey of Xia Qianjin, a Tsinghua University PhD graduate, who is now a professor at the Ordos Applied Technology College, contributing to the development of the Inner Mongolia Large Aircraft Academy and engaging in academic research and student mentorship [1][3]. Group 1: Academic Contributions - Xia Qianjin has published over 10 research papers and holds 2 public invention patents and 2 authorized utility model patents [3]. - He has successfully secured various research projects, including city-level, Inner Mongolia Natural Science Foundation, and National Natural Science Foundation projects, showcasing his capability in leading research initiatives [3][5]. Group 2: Student Engagement - Xia actively engages with students, conducting practical classes that enhance their hands-on skills and engineering thinking, such as aircraft engine model disassembly and 3D modeling [5][6]. - He is dedicated to student development, providing personalized feedback on their theses and offering career planning advice, which has positively impacted students' learning experiences [6]. Group 3: Work Environment - The supportive talent policies of Ordos City and the college's emphasis on attracting high-level talent have facilitated Xia's smooth transition into his role [3]. - Xia maintains two offices: one for academic research and another for student practice, allowing him to stay at the forefront of research while mentoring students [5].

Core Viewpoint - Peter Lax, the first applied mathematician to receive the Abel Prize, passed away at the age of 99, marking the end of an era in mathematics and science [1][49]. Group 1: Contributions to Mathematics - Lax was a pioneer in applying computer technology to mathematical analysis and made significant contributions that are still widely used in scientific research and engineering practices [4][5]. - His notable works include the classic textbook "Functional Analysis" and other widely appreciated texts such as "Calculus and Its Applications" and "Linear Algebra and Its Applications" [2][6][7]. - Lax's research spanned various fields, including partial differential equations, fluid mechanics, numerical computation, scattering theory, and integrable systems, leading to profound theoretical results and practical algorithms [33]. Group 2: Awards and Recognition - Lax received numerous prestigious awards, including the National Medal of Science in 1986 and the Wolf Prize in 1987, culminating in being the first applied mathematician to win the Abel Prize in 2005 for his foundational work in partial differential equations [35][36]. - The Abel Prize committee described him as "the most versatile mathematician of his generation," highlighting his broad impact on the field [37]. Group 3: Personal Background and Legacy - Born on May 1, 1926, in Budapest, Lax showed exceptional mathematical talent from a young age, influenced by his family and mentors [15][16][17]. - His experiences during World War II, including his work on the Manhattan Project, shaped his understanding of the importance of computation in science [23][24][25]. - Lax's legacy includes not only his research and publications but also his commitment to education and mentorship, having trained over 55 doctoral students [46].