Mario Ageno and the status of biophysics.

This essay focuses on Mario Ageno (1915-1992), initially director of the physics laboratory of the Italian National Institute of Health and later professor of biophysics at Sapienza University of Rome. A physicist by training, Ageno became interested in explaining the special characteristics of living organisms origin of life by means of quantum mechanics after reading a book by Schrödinger, who argued that quantum mechanics was consistent with life but that new physical principles must be found. Ageno turned Schrödinger's view into a long-term research project. He aimed to translate Schrödinger's ideas into an experimental programme by building a physical model for at least a very simple living organism. The model should explain the transition from the non-living to the living. His research, however, did not lead to the expected results, and in the 1980s and the 1990s he focused on its epistemological aspect, thinking over the tension between the lawlike structure of physics and the historical nature of biology. His reflections led him to focus on the nature of the theory of evolution and its broader scientific meaning.

Phase transitions and the birth of early universe particle physics.

This paper provides a conceptual history of the development of early universe particle physics in the 1970s, focusing on the development of more sophisticated tools for constructing gauge-theories at finite-temperature. I start with a focus on early investigations into spontaneous symmetry restoration, and continue through the development of functional methods up to equilibrium finite-temperature field theory. I argue that the early universe provides an ideal setting for integrated modelling of thermal, gravitational, and particle physics effects due to its relative simplicity. I further argue that the development of finite-temperature field theory played an important secondary role in the rise of the effective field theory worldview, and investigate the status of the analogies between phase transitions in particle physics and condensed matter physics. I find that the division into "formal" versus "physical" analogies is too coarse-grained to understand the important physical developments at play.

Computer simulation in data analysis: A case study from particle physics.

The paper presents a case study of the data analysis in the CDHS scattering experiment of particle physics performed in 1983. The case study compares the function of computer simulation in the data analysis with recent philosophical work on the role of simulations in high energy physics (HEP) and the theory-ladenness of the data. In the data analysis of CDHS, computer simulations entered an iterative process of probabilistic data correction. The computer simulation was a crucial ingredient of the data analysis that served to increase the accuracy of the measurement. The way in which simulation was used corresponds in a certain sense to the function of "models as mediators" (Morgan and Morrison), by mediating knowledge about measurement errors and the way of correcting them. I argue that this use of simulation did not give rise to a vicious circle of adjusting data to theory and vice versa but only to a weak, or benign, theory-ladenness of the data compatible with scientific realism. In the publication of the CDHS results, the measurement outcomes are called "observed data", indicating a realist attitude of the physicists towards the measured quantities which does not exactly fit in with entity realism or theory realism.

Towards complex dynamic physics system simulation with graph neural ordinary equations.

The great learning ability of deep learning facilitates us to comprehend the real physical world, making learning to simulate complicated particle systems a promising endeavour both in academia and industry. However, the complex laws of the physical world pose significant challenges to the learning based simulations, such as the varying spatial dependencies between interacting particles and varying temporal dependencies between particle system states in different time stamps, which dominate particles' interacting behavior and the physical systems' evolution patterns. Existing learning based methods fail to fully account for the complexities, making them unable to yield satisfactory simulations. To better comprehend the complex physical laws, we propose a novel model - Graph Networks with Spatial-Temporal neural Ordinary Differential Equations (GNSTODE) - that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework. Through training with real-world particle-particle interaction observations, GNSTODE can simulate any possible particle systems with high precisions. We empirically evaluate GNSTODE's simulation performance on two real-world particle systems, Gravity and Coulomb, with varying levels of spatial and temporal dependencies. The results show that GNSTODE yields better simulations than state-of-the-art methods, showing that GNSTODE can serve as an effective tool for particle simulation in real-world applications. Our code is made available at https://github.com/Guangsi-Shi/AI-for-physics-GNSTODE.

Tackling the curse of dimensionality with physics-informed neural networks.

The curse-of-dimensionality taxes computational resources heavily with exponentially increasing computational cost as the dimension increases. This poses great challenges in solving high-dimensional partial differential equations (PDEs), as Richard E. Bellman first pointed out over 60 years ago. While there has been some recent success in solving numerical PDEs in high dimensions, such computations are prohibitively expensive, and true scaling of general nonlinear PDEs to high dimensions has never been achieved. We develop a new method of scaling up physics-informed neural networks (PINNs) to solve arbitrary high-dimensional PDEs. The new method, called Stochastic Dimension Gradient Descent (SDGD), decomposes a gradient of PDEs' and PINNs' residual into pieces corresponding to different dimensions and randomly samples a subset of these dimensional pieces in each iteration of training PINNs. We prove theoretically the convergence and other desired properties of the proposed method. We demonstrate in various diverse tests that the proposed method can solve many notoriously hard high-dimensional PDEs, including the Hamilton-Jacobi-Bellman (HJB) and the Schrödinger equations in tens of thousands of dimensions very fast on a single GPU using the PINNs mesh-free approach. Notably, we solve nonlinear PDEs with nontrivial, anisotropic, and inseparable solutions in less than one hour for 1000 dimensions and in 12 h for 100,000 dimensions on a single GPU using SDGD with PINNs. Since SDGD is a general training methodology of PINNs, it can be applied to any current and future variants of PINNs to scale them up for arbitrary high-dimensional PDEs.

Historicizing a Dream of Complete Science.

This paper attempts an historical analysis of a dream of the physicist George Gamow recorded shortly before his death in 1968. The dream is contextualized through Gamow's extended scientific work and popular scientific efforts, and in light of enduring preoccupations with the notion of a complete science. The analysis extends to an examination of the relationship of the dream to dreaming practices and deliberations apart from Gamow's, as evident in the relationship and collaboration between the physicist Wolfgang Pauli and C. G. Jung.

Construction of a direct Z-scheme Cs3Bi2Cl9/g-C3N4 heterojunction composite for efficient photocatalytic degradation of various pollutants in water: Performance, kinetics and degradation mechanism.

The use of emerging composite materials has been booming to remove environmental pollutants. The aim of this research is to develop a new composite based on Cs3Bi2Cl9 perovskite and graphitic carbon nitride (g-C3N4) to investigate the photocatalytic performance under visible light irradiation. To achieve this, we produce the Cs3Bi2Cl9/g-C3N4 heterojunctions through a simple self-assembly synthesis. The as-synthesized composites are characterized using XRD, FTIR, FESEM, TEM, BET and EDX techniques. The photocatalytic performance of Cs3Bi2Cl9/g-C3N4 is examined in the degradation of various water contaminants, including 4-nitrophenol (4-NP), tetracycline antibiotic (TC), methylene blue (MB) and methyl orange (MO). The experimental results indicate the superior photocatalytic performance of the composites in the degradation of pollutants compared to pure Cs3Bi2Cl9 and g-C3N4. The 10% Cs3Bi2Cl9/g-C3N4 composite achieves the optimal degradation efficiency of 100, 92, 98.7, and 85.1% of 4-NP, TC, MB, and MO, respectively. This superior photocatalytic activity attributes to improved optical and electrochemical properties, including enhanced absorption ability, narrowing band gap, promoted separation efficiency of photogenerated carriers, and a high redox potential, which is confirmed by UV-vis DRS, PL, EIS, and CV analyses. The 10% Cs3Bi2Cl9/g-C3N4 composite also demonstrates high photocatalytic stability after four consecutive cycles. Radical trapping tests show that superoxide radicals (•O2-), holes (h+), and hydroxyl radicals (•OH) contribute to the photocatalytic process. Based on the obtained data, a direct Z-scheme heterojunction mechanism is proposed. Overall, this research offers a new stable photocatalyst with excellent prospect for photocatalytic applications.

On algebraic naturalism and metaphysical indeterminacy in quantum mechanics.

I propose a technique for identifying fundamental properties using structures already present in physical theories. I argue that, in conjunction with a particular naturalistic commitment, that I dub 'algebraic naturalism', these structures can be used to generate a standard of metaphysical determinacy. This standard can be used to rule out the possibility of a virulent strain of 'deep' metaphysical indeterminacy that has been imputed to quantum mechanics.

Catch bond kinetics are instrumental to cohesion of fire ant rafts under load.

Dynamic networks composed of constituents that break and reform bonds reversibly are ubiquitous in nature owing to their modular architectures that enable functions like energy dissipation, self-healing, and even activity. While bond breaking depends only on the current configuration of attachment in these networks, reattachment depends also on the proximity of constituents. Therefore, dynamic networks composed of macroscale constituents (not benefited by the secondary interactions cohering analogous networks composed of molecular-scale constituents) must rely on primary bonds for cohesion and self-repair. Toward understanding how such macroscale networks might adaptively achieve this, we explore the uniaxial tensile response of 2D rafts composed of interlinked fire ants (S. invicta). Through experiments and discrete numerical modeling, we find that ant rafts adaptively stabilize their bonded ant-to-ant interactions in response to tensile strains, indicating catch bond dynamics. Consequently, low-strain rates that should theoretically induce creep mechanics of these rafts instead induce elastic-like response. Our results suggest that this force-stabilization delays dissolution of the rafts and improves toughness. Nevertheless, above 35[Formula: see text] strain low cohesion and stress localization cause nucleation and growth of voids whose coalescence patterns result from force-stabilization. These voids mitigate structural repair until initial raft densities are restored and ants can reconnect across defects. However mechanical recovery of ant rafts during cyclic loading suggests that-even upon reinstatement of initial densities-ants exhibit slower repair kinetics if they were recently loaded at faster strain rates. These results exemplify fire ants' status as active agents capable of memory-driven, stimuli-response for potential inspiration of adaptive structural materials.

Physics-informed neural wavefields with Gabor basis functions.

Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs). Despite their potential, the training can be computationally demanding, especially for intricate functions like wavefields. This is primarily due to the neural-based (learned) basis functions, biased toward low frequencies, as they are dominated by polynomial calculations, which are not inherently wavefield-friendly. In response, we propose an approach to enhance the efficiency and accuracy of neural network wavefield solutions by modeling them as linear combinations of Gabor basis functions that satisfy the wave equation. Specifically, for the Helmholtz equation, we augment the fully connected neural network model with an adaptable Gabor layer constituting the final hidden layer, employing a weighted summation of these Gabor neurons to compute the predictions (output). These weights/coefficients of the Gabor functions are learned from the previous hidden layers that include nonlinear activation functions. To ensure the Gabor layer's utilization across the model space, we incorporate a smaller auxiliary network to forecast the center of each Gabor function based on input coordinates. Realistic assessments showcase the efficacy of this novel implementation compared to the vanilla PINN, particularly in scenarios involving high-frequencies and realistic models that are often challenging for PINNs.

A cutting tool architecture designed to address the parasitic mechanisms consuming excess power during machining and manufacturing operations-A review-based study towards sustainable manufacturing.

Metal cutting has been extensively studied over the years for improving its efficacy, yet, parasitic mechanisms like chatter and tool wear continue to generate higher forces and energy consumption with poor surface integrity. To address these parasitic mechanisms, a single-point turning cutter design is proposed based on the physics-of-machining including chatter theory to achieve reduced power consumption during the cutting of various metallic alloys like Al-6061, Ti-6Al-4V and others used by critical sectors such as aerospace and automotive. The current work focuses on aspects of machining that effectively reduce parasitic forces feeding into cutting power. The proposed cutter amalgamates features such as optimum side and end cutting edge angles, smaller nose radius and textured rake face into the cutter-body. Such a design is further proposed for use with a mechanochemical effect on a recently discovered plastic flow mode called sinuous flow, which has been reported to bring down cutting forces significantly. Experimental and analytical tests on the cutter design features validate reduction of cutting forces and through that alleviate the tendency to chatter as well as bring about energy savings for cutting of Al 6061. The potential for reduced real-time power consumption makes this design-framework significant for multipoint milling cutters too. It will greatly facilitate frugal manufacturing to account for sustainability in manufacturing operations.

BioID Analysis of Actin-Binding Proteins.

Proteins often exist and function as part of higher-order complexes or networks. A challenge is to identify the universe of proximal and interacting partners for a given protein. We describe how the high-activity promiscuous biotin ligase called TurboID is fused to the actin-binding peptide LifeAct to label by biotinylation proteins that bind, or are in close proximity, to actin. The rapid enzyme kinetics of TurboID allows the profiles of actin-binding proteins to be compared under different conditions, such as acute disruption of filamentous actin structures with cytochalasin D.

The epoch-making importance of Ervin Bauer's theoretical biology.

Ervin Bauer was the only biologist who recognized that the best way to develop theoretical biology on an equal footing with theoretical physics was to follow the method that has ensured the great successes of modern theoretical physics: the general method of science. Following this method, he succeeded to find the universal principle of biology. From this principle he managed to derive all the basic equations of biology, that of metabolism, reproduction, growth, responsiveness and successfully explained all the fundamental phenomena of life. In this paper, I introduce Bauer's theoretical biology and discuss whether he understood it within the framework of the modern physical worldview, or in a broader framework. I point out that the theoretical biology of Ervin Bauer is the first to go beyond the physical worldview, to establish a deeper, biological worldview, and thus to represent a major advance in our understanding of the nature of life, with a significance even greater than that of the Copernican turn. Clarifying the difference between the living and the non-living, it is important to consider the difference between machines and living organisms. It is well known that machines are the manifestations of a dual control; globally, their behavior is controlled by their given structure, while locally, their behavior is governed by the physical laws. Based on Bauer's theoretical biology, it is pointed out that living organisms manifest a three-level causality; the 'additional', biological level corresponds to the autonomous, time-dependent control of their structures.

DNA polyhedrons cube, prism, and square pyramid protect the catalytic activity of catalase: A thermodynamics and kinetics study.

DNA is widely used as building block material for the construction of polyhedral nanostructures. DNA polyhedrons (DNA prism, cube, and square pyramid) are small 3D wireframed nanostructures with tunable shapes and sizes. Despite substantial progress in synthesis, the study regarding cellular responses to DNA polyhedrons is limited. Herein, the molecular interaction between DNA polyhedrons and the antioxidant enzyme, catalase has been explored. The enzymatic activity of bovine liver catalase (BLC) remains unaltered in the presence of DNA polyhedrons after 1 h of incubation. However, the activity of BLC was protected after 24 h of incubation in the presence of DNA polyhedrons as compared to the natural unfolding. The kinetics study confirmed the protective role of DNA polyhedrons on BLC with lower KM and higher catalytic efficiency. Furthermore, no profound conformational changes of BLC occur in the presence of DNA polyhedrons as observed in spectroscopic studies. From fluorescence quenching data we confirmed the binding between DNA polyhedrons and BLC. The thermodynamic parameters indicate that non-covalent bonds played a major role during the interaction of BLC with DNA polyhedrons. Moreover, the hepatic catalase activity remains unaltered in the presence of DNA polyhedrons. The cytotoxicity assay revealed that DNA polyhedrons were biocompatible in the cellular environment. The protective role of DNA polyhedrons on enzyme activity and the unaltered conformational change of protein ensures the biocompatibility of DNA polyhedrons in the cellular environment.

The notorious man-in-the-street: Hermann Weyl and the problem of knowledge.

Hermann Weyl's philosophical reflections remain a topic of considerable interest in the history and philosophy of science. In particular, Weyl's commitment to a form of idealism, as it pertains to his reading of Husserl and Fichte, has garnered much discussion. However, much less attention has been given to Weyl's later, and at that only partial, turn towards a form of empiricism (i.e. from the late 1920s onward). This lack of focus on Weyl's later philosophy has tended to obscure some of the most significant lessons that Weyl sought to draw from his decades of research in the foundations of mathematics and physics. In this paper, I develop some aspects of what I will term as Weyl's 'modest' empiricism. I will argue that Weyl's turn toward empiricism can be read in the context of a development of Helmholtz's epistemological program and his unique form of 'Kantianism'. The hope is that this reading will not only provide a better understanding of Weyl's later thought, especially his (1954) criticism of Cassirer, but that it may also provide the basis for a novel 'Weylian' account of the mathematization of nature underwriting the group-theoretic methodology of parts of modern physics.

Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning.

We present an unsupervised deep learning method to perform flow denoising and super-resolution without high-resolution labels. We demonstrate the ability of a single model to reconstruct three-dimensional stenosis and aneurysm flows, with varying geometries, orientations, and boundary conditions. Ground truth data was generated using computational fluid dynamics, and then corrupted with multiplicative Gaussian noise. Auto-encoders were used to compress the representations of the flow domain geometry and the (possibly noisy and low-resolution) flow field. These representations were used to condition a physics-informed neural network. A physics-based loss was implemented to train the model to recover lost information from the noisy input by transforming the flow to a solution of the Navier-Stokes equations. Our experiments achieved mean squared errors in the true flow reconstruction of O(1.0 × 10-4), and root mean squared residuals of O(1.0 × 10-2) for the momentum and continuity equations. Our method yielded correlation coefficients of 0.971 for the hidden pressure field and 0.82 for the derived wall shear stress field. By performing point-wise predictions of the flow, the model was able to robustly denoise and super-resolve the field to 20× the input resolution.

Essay: Exploring the Physics of Basic Medical Research.

The 20th century witnessed the emergence of many paradigm-shifting technologies from the physics community, which have revolutionized medical diagnostics and patient care. However, fundamental medical research has been mostly guided by methods from areas such as cell biology, biochemistry, and genetics, with fairly small contributions from physicists. In this Essay, I outline some key phenomena in the human body that are based on physical principles and yet govern our health over a vast range of length and time scales. I advocate that research in life sciences can greatly benefit from the methodology, know-how, and mindset of the physics community and that the pursuit of basic research in medicine is compatible with the mission of physics. Part of a series of Essays that concisely present author visions for the future of their field.

Performance of Fourier-based activation function in physics-informed neural networks for patient-specific cardiovascular flows.

BACKGROUND AND OBJECTIVES: Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks are known to be biased towards learning less complex functions, called spectral bias. This has important implications in modeling cardiovascular flows, where spatial frequencies can vary substantially across anatomies and pathologies (e.g., aneurysms or stenoses). Recent evidence suggests that Fourier-based activation functions have desirable properties, and can potentially reduce spectral bias; however, the performance and adequacy of such Fourier activation functions have not yet been evaluated in patient-specific cardiovascular flow applications. METHODS: The performance of sine activation function was evaluated against tanh and swish activation functions in a 1D advection-diffusion problem, an eccentric 2D stenosis model (Re=5000), and a patient-specific 3D aortic model (Re=823) under pulsatile flow conditions. CFD simulations were performed at high spatio-temporal resolution and data points were extracted for training the neural network. The number of training data points were normalized by L/D. The performance of the PINNs framework was evaluated with increasing number of training data points and across all three activation functions. RESULTS: Our results demonstrate that sine activation function presents desirable characteristics, such as monotonic reduction in errors, relatively faster convergence, and accurate eigen spectra at higher modes, compared to tanh and swish activation functions. Interestingly, for all activation functions, the domain-averaged errors tended to asymptote at ≈15-20% despite substantial increase in training point density. For 2D eccentric stenosis, errors asymptoted at a sensor point density of 40L/D. For 3D patient-specific aorta, this asymptote was achieved at 180L/D for all three activation functions with an error of ≈15% although sine activation function demonstrated relatively faster convergence. CONCLUSIONS: We have demonstrated that Fourier-based activation functions have higher performance in terms of accuracy and convergence properties for cardiovascular flow applications; however, inherent challenges of neural networks (e.g., spectral bias) can limit the accuracy to ≈15% under physiological, 3D patient-specific blood flow conditions.