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Among about a thousand objects obtained by the Science Museum from Stephen Hawking's office is a blackboard full of cartoons and puns, both childishly silly and deeply scientifically meaningful. This blackboard is now one of the Museum's most popular objects thanks to its playful imagery and perhaps also its perceived fragility. But it is also one of the objects that best illustrates the scientific life of Stephen Hawking and his generation, and more broadly, it is an exemplar of the social and material culture of the postmodern epoch of theoretical physics in the decades around the turn of the twenty-first century. In this article, the curator of the Hawking Collection explains the importance of this blackboard, first through an account of its unexpected trajectory to becoming the Hawking Collection's most charismatic object, followed by a discussion of the general historical role of blackboards: their iconography, material culture, and value as historical sources and how their conservation requirements contribute to their perceived value. The author then delves deeper into the specific content of this blackboard, showing how it constitutes a window into the highs and lows of a generation of physicists inspired by Hawking's work, as they attempted, and ultimately failed, to unify the two great theories of the twentieth century: general relativity and quantum mechanics. What we now call the superspace and supergravity blackboard is a microcosm of one of those moments in physics when theory broke away from what was empirically verifiable. The scribblings on the blackboard were collectively drawn by an up-and-coming generation of supersymmetry theorists. It immortalises the promising early years of supergravity, one of the hopeful candidates at the time for the long-wished-for Theory of Everything. Preserved in chalk are the seeds of one of the most brilliant theoretical discoveries of the turn of the century: what is now called the holographic principle. But also, seen with the benefit of hindsight, supersymmetric approaches seeded the identity crisis that theoretical physics is experiencing today, especially around the role of empirical anchoring of proposed fundamental theories. The contested status of the blackboard's content and its humorous, self-ironic style, make it a key piece of a unique zeitgeist of physics at the turn of the twenty-first century, which one could call the era of postmodern physics. In addition to its significance to the history of specific ideas, this paper also argues that the blackboard reflects the collective, social nature of theoretical research, while also inviting reflection on what constitutes success and failure in the physical sciences. [ABSTRACT FROM AUTHOR]

Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of the system, revealing the approximate nature of the equations and/or the signal-to-noise ratio of the sensor itself. In modern dynamical systems, such discrepancies between model and measurement can lead to poor quantification, often undermining the ability to produce accurate and precise control algorithms. We introduce a discrepancy modeling framework to identify the missing physics and resolve the model-measurement mismatch with two distinct approaches: (i) by learning a model for the evolution of systematic state-space residual, and (ii) by discovering a model for the deterministic dynamical error. Regardless of approach, a common suite of data-driven model discovery methods can be used. Specifically, we use four fundamentally different methods to demonstrate the mathematical implementations of discrepancy modeling: (i) the sparse identification of nonlinear dynamics, (ii) dynamic mode decomposition, (iii) Gaussian process regression, and (iv) neural networks. The choice of method depends on one's intent (e.g., mechanistic interpretability) for discrepancy modeling, sensor measurement characteristics (e.g., quantity, quality, resolution), and constraints imposed by practical applications (e.g., state- or dynamical-space operability). We demonstrate the utility and suitability for discrepancy modeling using the suite of data-driven modeling methods on four dynamical systems under varying signal-to-noise ratios. Finally, we emphasize structural shortcomings of each discrepancy modeling approach depending on error type. In summary, if the true dynamics are unknown (i.e., an imperfect model), one should learn a discrepancy model of the missing physics in the dynamical space. Yet, if the true dynamics are known yet model-measurement mismatch still exists, one should learn a discrepancy model in the state space. [ABSTRACT FROM AUTHOR]

Although physics does not often veer into financial concerns, it is an illuminating exercise to try estimating the value of a barren planet at market prices for dirt, rock, water, copper, aluminum, silver, gold, etc. Energy perspectives Physics "owns" energy (and space and time if we're being greedy), so energy and power domains offer rich opportunities for gaining new understanding. The thermodynamic consequences of this absurdity can also be examined, by which it is revealed that waste heat from all this energy production on Earth would rival climate change in a century and be 10 times worse a century after that, eventually boiling Earth's oceans in about 400 yr.[3] It can be tricky to convey to students that this is not a I prediction i of what I will i - or even I can i - happen, but a tool to understand I limits i to what is possible and what I cannot i transpire. [Extracted from the article]

In my context, such a class might be a general physics course for science majors (pre-calculus- or calculus-based) or an algebra-based, mostly conceptual survey course for non-science majors, with between 8 and 30 students per class. Homework assignments, reflections, surveys, and biweekly anonymous feedback from 2017 onward (excepting 2020) from general physics courses for science majors and for non-science majors indicate enhanced interest in physics, greater cognitive understanding of climate science basics, increased appreciation of the value of climate stories and justice issues, ability to think critically about proposed climate solutions, and, for the majority of students (in some classes greater than 80%), a desire to take meaningful action for climate justice. Today, I still teach the basic science of climate change as it aligns with physics topics in my general physics courses. [Extracted from the article]

Even as the physical sciences have advanced and transformed, many of the community's needs and concerns have persisted. [ABSTRACT FROM AUTHOR]

On the left is one, ca. 1910, that is in my collection of early physics teaching apparatus.[3] Two light bulbs illuminate a postcard that serves as an object for the lens, and an image is projected on a white sheet. 1 has an objective lens with a focal length of 15 cm and a second lens with a focal length of 7 cm. The second lens is placed 5 cm inside a smaller sliding box. In this note, I will describe five pieces of apparatus: a telescope, a microscope, a galvanometer, an opaque projector, and an air table that I built for my boys to bring them into the worlds of physical science. The image formed by the first lens was inverted, and the magnified image obtained with the second lens was similarly inverted. [Extracted from the article]

Physics‐driven discovery in an autonomous experiment has emerged as a dream application of machine learning in physical sciences. Here, this work develops and experimentally implements a deep kernel learning (DKL) workflow combining the correlative prediction of the target functional response and its uncertainty from the structure, and physics‐based selection of acquisition function, which autonomously guides the navigation of the image space. Compared to classical Bayesian optimization (BO) methods, this approach allows to capture the complex spatial features present in the images of realistic materials, and dynamically learn structure–property relationships. In combination with the flexible scalarizer function that allows to ascribe the degree of physical interest to predicted spectra, this enables physical discovery in automated experiment. Here, this approach is illustrated for nanoplasmonic studies of nanoparticles and experimentally implemented in a truly autonomous fashion for bulk‐ and edge plasmon discovery in MnPS3, a lesser‐known beam‐sensitive layered 2D material. This approach is universal, can be directly used as‐is with any specimen, and is expected to be applicable to any probe‐based microscopic techniques including other STEM modalities, scanning probe microscopies, chemical, and optical imaging. [ABSTRACT FROM AUTHOR]

The IPCC's scientific assessment of the timing of net‐zero emissions and 2030 emission reduction targets consistent with limiting warming to 1.5°C or 2°C rests on large scenario databases. Updates to this assessment, such as between the IPCC's Special Report on Global Warming of 1.5°C (SR1.5) of warming and the Sixth Assessment Report (AR6), are the result of intertwined, sometimes opaque, factors. Here we isolate one factor: the Earth System Model emulators used to estimate the global warming implications of scenarios. We show that warming projections using AR6‐calibrated emulators are consistent, to within around 0.1°C, with projections made by the emulators used in SR1.5. The consistency is due to two almost compensating changes: the increase in assessed historical warming between SR1.5 (based on AR5) and AR6, and a reduction in projected warming due to improved agreement between the emulators' response to emissions and the assessment to which it is calibrated. Plain Language Summary: The IPCC's latest physical science report, the Working Group 1 Contribution to the Sixth Assessment Report (AR6), was released in August 2021. That report includes an update to the tools used to project the climate outcome of emission scenarios. Here we apply these newly calibrated tools, called earth system model emulators, to the set of scenarios assessed in the IPCC's Special Report on warming of 1.5°C (SR1.5). We find that two compensating changes lead to a remarkable consistency (peak warming projections within 0.1°C) between the projections made by the emulators used in SR1.5 and their descendants used in AR6. First, updates to the historical warming assessment since the SR1.5 (which was based on the IPCC's 2013 physical science report (AR5)) increase future warming projections. However, improved consistency between the emulators and the assessment of the underlying physics, particularly the short‐term warming response to emissions, lowers warming projections by an approximately equivalent amount. Our work reinforces the key messages from the IPCC: limiting warming to around 1.5°C is a great and urgent challenge, and it is up to us to decide whether we pull out all the stops to hold temperatures around 1.5°C or whether we sail on by. Key Points: Emulators used in IPCC Special Report on warming of 1.5°C and Sixth Assessment Report are remarkably consistent, despite their entirely new calibrationsThe consistency is due to two compensating factors: change in assessed historical warming and improvements to emulator calibration methods [ABSTRACT FROM AUTHOR]

This article is based on the annual reports presented by the Head of the Physical Sciences Division of the Russian Academy of Sciences at the General Meetings of the Division in April 2021 and April 2022. It describes some scientific results in physics obtained in 2020–2021 at the research institutions (institutes of the Russian Academy of Sciences) under the individual powers of the Russian Academy of Sciences stipulated by the regulations of the Government of the Russian Federation no. 521 dated June 5, 2014, and no. 1652 dated December 24, 2018. The information about the research in different fields of physics carried out at these institutions is summarized briefly and illustrated by examples of some bright scientific breakthroughs. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has emerged as a formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In recent years, it is safe to conclude that ML and its close cousin, deep learning (DL), have ushered in unprecedented developments in all areas of physical sciences, especially chemistry. Not only classical variants of ML, even those trainable on near-term quantum hardwares have been developed with promising outcomes. Such algorithms have revolutionized materials design and performance of photovoltaics, electronic structure calculations of ground and excited states of correlated matter, computation of force-fields and potential energy surfaces informing chemical reaction dynamics, reactivity inspired rational strategies of drug designing and even classification of phases of matter with accurate identification of emergent criticality. In this review we shall explicate a subset of such topics and delineate the contributions made by both classical and quantum computing enhanced machine learning algorithms over the past few years. We shall not only present a brief overview of the well-known techniques but also highlight their learning strategies using statistical physical insight. The objective of the review is not only to foster exposition of the aforesaid techniques but also to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms. [ABSTRACT FROM AUTHOR]

If Homberg was mostly part of the "penumbra" of the Académie during Louvois's time in office, Homberg's was one of the first appointments made under Pontchartrain, who also directed the 1699 organizational reform. (p. 197) "We cannot raise chemistry", Venel complained half a century later, "to the level it deserves by demonstrating its philosophical side [...] we cannot do for chemistry what elegant machines, optics, and electricity have done for physics."[15] There is a sense in which both Homberg and Venel were right in these pronouncements. In this foundational conflict, whose origins predate Homberg's arrival at the Académie, those who envisioned chymistry as an integral part of natural philosophy, like Duclos and Homberg, are set against those who viewed it as an adjunct tool to medicine and pharmacy, like Denis Dodart. Contrary to its received image, the essence of chymistry is not "less neat and simple than the spirit of physics"; nor can physics be "superior" to a science that "can penetrate certain bodies of which physics knows only the surface and the outside shape."[14] Or, as Homberg put it in contrasting " I physique chimique i " ("chymical natural philosophy") and " I la physique i ", chymistry "easily explains its own operations in its own way and thereby knows more distinctly the substances it examines.". [Extracted from the article]

The research reported here examined the outcome of using interactive simulation technology (IST) as a guided-inquiry approach to enhance learners' conceptual knowledge of electrostatics in physics. Participants were Grade 11 physical sciences learners (n = 60) and a teacher from a rural school in South Africa. Learners were randomly assigned to an experimental group (n = 30) that took part in the intervention lesson using the integration of IST in the science classroom, and a control group (n = 30) that continued with the conventional teaching method. We adopted a mixed-method approach for this research. Data were collected through a pre-post achievement instrument, classroom observations, and focus group interviews. Data were analysed using the Mann-Whitney U-test, the Wilcoxon signed-rank test, and content analysis. It was found that the mean rank rating of the pre-test results for learners in both groups was not significantly different. However, the Mann-Whitney U-test indicated that learners' conceptual understanding measured in the post-test result was greater for the experimental group (mean rank gain score = 38.83) compared to the control group (mean rank gain score = 22.17), U = 200.0, p = 0.000185. This finding indicates that integrating IST into inquiry-based activities can be used efficiently to improve learners' in-depth knowledge of science concepts. [ABSTRACT FROM AUTHOR]

We describe the development, design, implementation, and preliminary classroom results of an innovative curriculum, Focus on Energy, that supports learning about energy in Grades 4–5. The curriculum is grounded in the concepts of science as practice, model‐based reasoning, and learning progressions, and builds on students' pre‐existing ideas and resources. We illustrate how students gradually develop the ability to track energy forms, transfers, and transformations in increasingly complex scenarios. We present evidence, using a quasi‐experimental design, that students who completed the curriculum were significantly more adept at these skills than students in comparable classrooms who experienced their districts' existing energy‐related physical science curricula. Important features of the curriculum include: the careful selection of a limited set of concepts chosen to provide a sound foundation for future learning; a consistent conceptual framework (the Energy Tracking Lens) within which the students have agency to build and refine a model of energy; engaging hands‐on activities that steadily build in complexity; accessible and versatile semi‐quantitative representations that support reasoning and communication; individual, small‐group and large‐group meaning‐making; and training and support for the teachers. [ABSTRACT FROM AUTHOR]

The current models of One Health appear to be suffering from a elevated emphasis on a small area of interface involving humans, environment and animals. This may largely be because of an extension of our understanding of physical science to biological world, which, though is far more complexer. [ABSTRACT FROM AUTHOR]

The article discusses that collisions are fundamentally important to the study of atomic and molecular gases . For example, they dictate the lifetimes of ultracold samples and the efficiency of cooling by evaporation. Ultracold collisions are largely governed by quantum mechanics and are very sensitive to electromagnetic fields.

The article discusses about cavitronics, a portmanteau of cavity and electronics which are devices with certain properties that can be controlled by the light waves bouncing inside the cavity in which the device sits. In quantum mechanical terms, this interaction between light and matter is done by the standing light waves inside the cavity known as vacuum field states.

The article discusses that physics departments at U.S. universities faced an existential crisis. The number of undergraduate physics majors had plummeted by 25% over 10 years, prompting fears that many departments might disappear or be merged into other programs. Academic physicists scrambled for ways to attract more students, making lectures more interactive and updating the curriculum.

An editorial is presented on the studies on the fundamental physics of silicon led to the device that makes it possible to read this article online. The convector of the transistor, William Shockley, who along with John Bardeen and Walter Brattain won the 1956 Nobel Prize in Physics, is correctly recognized as a primary architect of the computer age. Gordon Moore (cofounder of Intel Corporation) famously said that Shockley put the silicon in "Silicon Valley.

Each year we prepare rosters of all degree-granting physics and astronomy departments in the U.S. The institutions are sorted by state. For more data about physics in U.S. institutions and what people with physics degrees do, please visit www.aip.org/statistics. [Extracted from the article]