Physical Laws Research Articles

Perceiving visual events uses optical information that reflects dynamics rather than resembles appearance.

This study investigates the optical information for visual event perception. Events are objects in motion, with properties like shape, weight and surface material influencing the dynamics that shape movements and optics. The progressive transformation of visible textures, known as visual kinaesthetic information, specifies movements and objects. Four experiments tested whether events could be perceived using only visual kinaesthetic information. Participants identified their own walking from point-light displays (Experiment 1), from simulated environmental texture transformations as a result of their walking (Experiment 2), and from videos shot by a head-mounted camera during outdoor walking (Experiment 3); and distinguishing strangers from footages captured by their head-mounted cameras (Experiment 4). In Experiments 2-4, the displays did not resemble the outline of a person or look like walking but revealed the physical relations between the walker and the environment as a result of their movement. Regardless, participants were able to recognize themselves and distinguish strangers. Thus, observers are able to perceive events using visual kinaesthetic information that stems from dynamics. The one-to-one correspondences between object property, dynamics, kinematics and optical information are governed by the laws of physics, and unaffected by the event's appearance or viewing perspectives.

The Fascinating Evolution of Quantum Cryptography: The Modern Information Maginot Line

During the Cold War, a revolutionary cryptographic method, quantum cryptography, emerged. Grounded in the laws of quantum physics, quantum cryptography ensures absolute security using quantum principles. Stephen Wiesner introduced the concept in 1983, but initially received little attention, as technology at the time did not allow the encryption method to be put into practice. The field of quantum cryptography began to take shape with the arrival of two more researchers, Giles Brassard and Charles Bennett. The development of quantum cryptography accelerated dramatically after Artur Ekert proposed the application of Bell’s theorem in this field. Afterwards, more researchers got involved and new theorems and applications were developed, leading the field to become a major subject at the forefront of physics research. This paper explores why quantum cryptography is exceptionally secure and the steps that brought this idea to practice. The paper looks into the intriguing concept of quantum cryptography and the stimulating stories surrounding its progress. From Stephen Wiesner’s original proposal through Brassard and Bennett’s persistent efforts to the exciting debates between Ekert and Brassard and Bennett. This paper highlights the significant milestones and achievements that have positioned quantum cryptography at the forefront of physics research.

Advancements of Exploiting Convolutional Neural Networks for Solving Differential Equations

Solving Partial Differential Equations (PDEs) is essential across various fields, including physics, mathematics, and engineering. This study explores innovative methods for solving PDEs using Convolutional Neural Networks (CNNs). Traditionally, solving PDEs through numerical methods like finite difference or spectral approaches is computationally intensive, particularly when addressing high-dimensional problems and complex boundary conditions. CNNs, with their ability to handle spatial data efficiently, offer a promising alternative. This research evaluates different neural network architectures, including data-driven, physics-driven, and hybrid approaches, and their role in enhancing the accuracy and scalability of PDE solutions. Data-driven methods rely on extensive datasets for training neural networks, while Physics-Informed Neural Networks (PINNs) integrate the underlying physical laws into the solution process. Hybrid approaches combine both strategies, leveraging data and physical principles. The paper emphasizes CNNs potential to transform traditional PDE-solving methods, making them more efficient and adaptable to complex, real-world applications. Future research directions include optimizing these methods for greater scalability and interdisciplinary applications.

TorchSISSO: A PyTorch-based implementation of the sure independence screening and sparsifying operator for efficient and interpretable model discovery

Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact representations of complex data. Unlike traditional regression methods, SR explores progressively complex feature spaces, which can uncover simple models that generalize well, even from small datasets. Among SR algorithms, the Sure Independence Screening and Sparsifying Operator (SISSO) has proven particularly effective in the natural sciences, helping to rediscover fundamental physical laws as well as discover new interpretable equations for materials property modeling. However, its widespread adoption has been limited by performance inefficiencies and the challenges posed by its FORTRAN-based implementation, especially in modern computing environments. In this work, we introduce TorchSISSO, a native Python implementation built in the PyTorch framework. TorchSISSO leverages GPU acceleration, easy integration, and extensibility, offering a significant speed-up and improved accuracy over the original. We demonstrate that TorchSISSO matches or exceeds the performance of the original SISSO across a range of tasks, while dramatically reducing computational time and improving accessibility for broader scientific applications.

Distortions in Classical Mechanics as a Consequence of Euclidean Geometry Utilization

We live on a huge sphere. When the sum of interior angles in a triangle is greater than [Formula: see text] and each point has a different local vertical, we must use spherical geometry. Euclidean and non-Euclidean geometries are non-equivalent systems, with their own premises and completely different foundations. The use of any logic and tool from orthogonal into non-orthogonal realities (and vice versa), leads to big errors. Newtonian concepts are treated as absolutes in classical mechanics. Content analysis shows that even recent physics publications have inadmissible distortions in the description of natural phenomena in the domain of classical mechanics, inherited through the utilization of the Euclidean model. The whole objective reality has been presented using the tools of Euclidean geometry. When we use the tools of spherical geometry to examine the same phenomena, we get very different findings. The Law of conservation of energy appears as the most efficient tool for the evaluation of these significantly different findings. It confirms the validity of the new discoveries presented in this paper and gives legitimacy to the whole project! Classical mechanics becomes more consistent with the conservation laws of Physics, and the subject’s coherence is lifted to a significantly higher level.

A Physically Constrained Deep-Learning Fusion Method for Estimating Surface NO2 Concentration from Satellite and Ground Monitors.

Accurate estimation of atmospheric chemical concentrations from multiple observations is crucial for assessing the health effects of air pollution. However, existing methods are limited by imbalanced samples from observations. Here, we introduce a novel deep-learning model-measurement fusion method (DeepMMF) constrained by physical laws inferred from a chemical transport model (CTM) to estimate NO2 concentrations over the Continental United States (CONUS). By pretraining with spatiotemporally complete CTM simulations, fine-tuning with satellite and ground measurements, and employing a novel optimization strategy for selecting proper prior emission, DeepMMF delivers improved NO2 estimates, showing greater consistency and daily variation alignment with observations (with NMB reduced from -0.3 to -0.1 compared to original CTM simulations). More importantly, DeepMMF effectively addressed the sample imbalance issue that causes overestimation (by over 100%) of downwind or rural concentrations in other methods. It achieves a higher R2 of 0.98 and a lower RMSE of 1.45 ppb compared to surface NO2 observations, overperforming other approaches, which show R2 values of 0.4-0.7 and RMSEs of 3-6 ppb. The method also offers a synergistic advantage by adjusting corresponding emissions, in agreement with changes (-10% to -20%) reported in the NEI between 2019 and 2020. Our results demonstrate the great potential of DeepMMF in data fusion to better support air pollution exposure estimation and forecasting.

Time arrow without past hypothesis: a toy model explanation

Abstract The laws of Physics are time-reversible, making no qualitative distinction between the past and the future—yet we can only go towards the future. This apparent contradiction is known as the ‘arrow of time problem’. Its current resolution states that the future is the direction of increasing entropy. But entropy can only increase towards the future if it was low in the past, and past low entropy is a very strong assumption to make, because low entropy states are rather improbable, non-generic. Recent works from the Physics literature suggest, however, we may do away with this so-called ‘past hypothesis’, in the presence of reversible dynamical laws featuring expansion. We prove that this is the case in principle, by means of a toy model. The discrete setting of this model allows us to deploy the full rigour of theoretical Computer Science proof techniques. It also allows for numerical exploration. We study a time-symmetric variant; an exponential-growth variant; and a damping variant—exhibiting similar features. The latter slows down thermal death.

Successful Precipitation Downscaling Through an Innovative Transformer-Based Model

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model.

Physical reservoir computing: a tutorial

This tutorial covers physical reservoir computing from a computer science perspective. It first defines what it means for a physical system to compute, rather than merely evolve under the laws of physics. It describes the underlying computational model, the Echo State Network (ESN), and also some variants designed to make physical implementation easier. It explains why the ESN model is particularly suitable for direct physical implementation. It then discusses the issues around choosing a suitable material substrate, and interfacing the inputs and outputs. It describes how to characterise a physical reservoir in terms of benchmark tasks, and task-independent measures. It covers optimising configuration parameters, exploring the space of potential configurations, and simulating the physical reservoir. It ends with a look at the future of physical reservoir computing as devices get more powerful, and are integrated into larger systems.

Physics-informed two-tier neural network for non-linear model order reduction

In recent years, machine learning (ML) has had a great impact in the area of non-intrusive, non-linear model order reduction (MOR). However, the offline training phase often still entails high computational costs since it requires numerous, expensive, full-order solutions as the training data. Furthermore, in state-of-the-art methods, neural networks trained by a small amount of training data cannot be expected to generalize sufficiently well, and the training phase often ignores the underlying physical information when it is applied with MOR. Moreover, state-of-the-art MOR techniques that ensure an efficient online stage, such as hyper reduction techniques, are either intrusive or entail high offline computational costs. To resolve these challenges, inspired by recent developments in physics-informed and physics-reinforced neural networks, we propose a non-intrusive, physics-informed, two-tier deep network (TTDN) method. The proposed network, in which the first tier achieves the regression of the unknown quantity of interest and the second tier rebuilds the physical constitutive law between the unknown quantities of interest and derived quantities, is trained using pretraining and semi-supervised learning strategies. To illustrate the efficiency of the proposed approach, we perform numerical experiments on challenging non-linear and non-affine problems, including multi-scale mechanics problems.

A novel heterogeneous deformable surface model based on elasticity

The thin membranes and shells in nature are heterogeneous. They are widely used in surgical simulation, biological techniques, and computer animation. The corresponding surface deformable models can implement dynamic simulations of thin membranes and shells in nature, while most surface deformable models are isotropic and cannot represent thin membranes and shells in nature accurately. Therefore, we propose a novel physically-based heterogeneous deformable surface model. By utilizing the same B-spline basis functions or the parameter space of surfaces' geometric representations, we implement material modeling and propose the representations of surfaces with material variations with composite or continuous material functions. Then, we propose a novel physically-based elastic deformable surface model that constructs infinitesimal elements in the parameter space and employs elasticity to analyze their deformation. The corresponding elastic potential energy function is only related to surfaces' continuous representations, and our model avoids the computation error caused by meshes' quality and large rotation of points' frames. We employ isogeometric analysis to solve the dynamic equations derived from our surface model. To demonstrate the validity and reality of our model, several comparison experiments are designed. The corresponding results are in line with expectations and consistent with physical laws.

Comment on Thomas Bradley’s IPI lecture

This Communication article was inspired by Thomas Bradley’s IPI Lecture, ”The Laws of Physics Embodied by Fundamental Replicators: Exploring Femes”, delivered on the 9th of Nov. 2024 to the members of the Information Physics Institute [1,2].

Quantifying uncertainty in neural network predictions of forced vibrations

AbstractThe prediction of forced vibrations in nonlinear systems is a common task in science and engineering, which can be tackled using various methodologies. A classical approach is based on solving differential (algebraic) equations derived from physical laws ('first principles'). Alternatively, Artificial Neural Networks (ANNs) may be applied, which rely on learning the dynamics of a system from given data. However, a fundamental limitation of ANNs is their lack of transparency, making it difficult to understand and trust the model's predictions. In this contribution, we follow a hybrid modelling approach combining a data‐based prediction using a stabilised Autoregressive Neural Network (s‐ARNN) with a priori knowledge from first principles. Moreover, aleatoric and epistemic uncertainty is quantified by a combination of mean‐variance estimation (MVE) and deep ensembles. Validating this approach for a classical Duffing oscillator suggests that the MVE ensemble is the most accurate and reliable method for prediction accuracy and uncertainty quantification. These findings underscore the significance of understanding uncertainties in deep ANNs and the potential of our method in improving the reliability of predictive nonlinear system modelling. We also demonstrate that including partially known dynamics can further increase accuracy, highlighting the importance of combining ANNs and physical laws.

Nanotechnology-enhanced Phytonutraceuticals: Innovations in Characterization, Formulation, and Therapeutic Applications

Nanotechnology refers to the branch of science and engineering devoted to designing, producing, and using structures, devices, and systems by manipulating atoms and molecules at nano-scale, having one or more dimensions of the order of 100 nanometres (100 millionth of a millimetre) or less. Nanotechnology is the exploitation of the unique properties of materials at the nanoscale. Nanotechnology has gained popularity in several industries, as it offers better built and smarter products. The ability to manipulate structures at the atomic scale allows for the creation of nanomaterials. Nanomaterials have unique optical, electrical and/or magnetic properties at the nanoscale, and these can be used in the fields of electronics and medicine, amongst other scenarios. Nanomaterials are unique as they provide a large surface area to volume ratio. Unlike other large-scaled engineered objects and systems, nanomaterials are governed by the laws of quantum mechanics instead of the classical laws of physics and chemistry. Nanotechnology is the engineering of useful objects and functional systems at the molecular or atomic scale. Nano-formulations are widely used for phyto-nutraceuticals and drug delivery systems. These substances are generally having low solubility, leading to their poor absorption and bioavailability in the human body. In this regard, one of the most important applications of nanotechnology in food sector has been the formulation of novel neutraceutical compounds with improved properties viz., enhanced solubility, stability, bioavailability and efficacy. This is achieved by encapsulation of neutraceutical by nanoparticles, which modifies their pharmacokinetics (PK) and biodistribution (BD). Nano-formulations widely used for the purpose are nanoliposomes, nanoemulsions, nanoparticles, nanofibres. The particles are characterized by scanning probe microscope (SPM), Ultraviolet-visible spectroscopy (UV-Vis spectrophotometer, SEM, TEM, Dynamic light scattering (DLS).

Unpacking the COVID-19 roadway fatality paradox: an analysis of motor vehicle crashes in Michigan 2019-2022

Objectives Motor vehicle travel shifts during the COVID-19 pandemic resulted in fewer vehicle miles traveled yet paradoxically higher fatality rates. Anecdotally, the paradox was blamed on increases in risky behavior in the absence of regular traffic and enforcement. We examined three hypotheses to explain the fatality paradox using Michigan crash data: (1) lack of congestion led to higher-speed impacts; (2) increased risky driver/driving; and (3) low-risk driving miles decreased. Methods We examined changes in hypotheses-related crash factors and injury outcomes for the years 2019–2022; restricted to the duration of Michigan’s stay-at-home order in 2020 (March 23 and June 1). First, we used logistic regression to evaluate the prevalence of the crash factors in all crashes and in severe/fatal crashes only in 2020, 2021; and 2022 compared to 2019. Second, to identify whether those crash factors differentially resulted in worse injury outcomes, we used logistic regression to evaluate whether odds of a severe/fatal crash occurring in March 23–June 1 across the years 2019–2022. Results All three hypotheses tested to explain Michigan’s traffic safety paradox had moderate to extensive support. Risky driving/driver factors were more prevalent during the 2020 stay-at-home order in all crashes; however, the risky driving factors were largely not more prevalent in severe and fatal injury crashes in 2020. In contrast, although less prevalent, many factors associated with the low-risk mileage and congestion hypotheses were more likely to result in severe and fatal outcomes during the stay-at-home orders in 2020. The prevalence of most congestion and low-risk miles factors remained less prevalent in 2020–2022 compared to 2019; while the prevalence of risky driving and driver factors in crashes had largely returned to 2019 levels or lower in 2022. Conclusions The overlapping contributions of the three different hypotheses explaining Michigan’s pandemic traffic safety paradox underscore the complexity of roadway safety and the need for simultaneous investments in driving education, environmental infrastructure, and technology-based mechanisms of enforcement. In the post-pandemic new normal, system-wide changes from investments like these can encourage safer driving even in the absence of social pressure or physical law enforcement presence.

An Effective Discontinuous Galerkin Method for Solving Acoustic Wave Equations in Heterogeneous Media

Abstract A discontinuous Galerkin (DG) method is introduced for solving 2D and 3D first-order velocity-pressure acoustic wave propagation problems in heterogeneous media. First, acoustic equations are reformulated into a first-order hyperbolic system, which can be integrated into the framework of the DG method. Then, we introduce an effective numerical flux in DG spatial discretization, which preserves physical laws on both sides of interfaces with discontinuous material parameters. It is compared with both the classic Lax-Friedrichs flux and the exact upwind flux, with the latter derived from solving the Riemann problem and satisfying the Rankine-Hugoniot condition. The comparison reveals that while our flux is formally similar to the classic local Lax-Friedrichs flux, its numerical behavior is analogous to the exact upwind flux. The primary advantage of this method lies in its simplicity and straightforward computation yet can maintain physical continuity conditions near interfaces with discontinuous material parameters. Several numerical experiments of acoustic wave propagation in 2D and 3D heterogeneous media are carried out, illustrating the effectiveness of this method.

Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization

Abstract Both computational and experimental material discovery bring forth the challenge of exploring multidimensional and often nondifferentiable parameter spaces, such as phase diagrams of Hamiltonians with multiple interactions, composition spaces of combinatorial libraries, processing spaces, and molecular embedding spaces. Often these systems are expensive or time consuming to evaluate a single instance, and hence classical approaches based on exhaustive grid or random search are too data intensive. This resulted in strong interest toward active learning methods such as Bayesian optimization (BO) where the adaptive exploration occurs based on human learning (discovery) objective. However, classical BO is based on a predefined optimization target, and policies balancing exploration and exploitation are purely data driven. In practical settings, the domain expert can pose prior knowledge of the system in the form of partially known physics laws and exploration policies often vary during the experiment. Here, we propose an interactive workflow building on multifidelity BO (MFBO), starting with classical (data-driven) MFBO, then expand to a proposed structured (physics-driven) structured MFBO (sMFBO), and finally extend it to allow human-in-the-loop interactive interactive MFBO (iMFBO) workflows for adaptive and domain expert aligned exploration. These approaches are demonstrated over highly nonsmooth multifidelity simulation data generated from an Ising model, considering spin–spin interaction as parameter space, lattice sizes as fidelity spaces, and the objective as maximizing heat capacity. Detailed analysis and comparison show the impact of physics knowledge injection and real-time human decisions for improved exploration with increased alignment to ground truth. The associated notebooks allow to reproduce the reported analyses and apply them to other systems.2

Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors

Transfer learning (TL) has the inspiring potential for artificial intelligence in heating, ventilation and air conditioning (HVAC) system with insufficient data labels. However, traditional TL-based methods are limited when applied across different conditions, systems, and operations.Unfortunately, public building HVAC systems encounter challenges related to data acquisition and richness, making it difficult to obtain data from similar HVAC systems conditions, scenarios and operations. It proposes a novel TL-based method that combines energy and mass balance constraint equation (EBCe) to diagnose the sensor faults in HVAC systems across different systems, conditions and operations.Firstly, it utilizes laboratory data as the source domain data and constructes EBCe based on the common physical laws of HVAC system to reduce the data differences between laboratory and public buildings. Then, an laplacian kernel domain-adaptive neural network (LkDaNN) is proposed to generalize more efficiently feature differences between the source domain data and target domain data. Finally, experiment analyzes the non-fault and four control-sensors fault under both cross-operation and non-cross operation conditions. The experimental results demonstrate that the EBCe-LkDaNN method achieves satisfactory fault detection and diagnosis (FDD) performance.The overall FDD accuracy of porposed method can reach 90.72 % and 88.64 % under different cross-operation, respectively. Practical application of the EBCe-LkDaNN strategy for HVAC sensor FDD are discussed at last.

Prediction of microstructural evolution of multicomponent polymers by Physics-Informed neural networks

As emerging concepts, leveraging physical information with machine learning to construct hybrid models enables a scientific data-driven paradigm for the investigation of complex kinetic issues in the field of polymer science. Herein, the model of physics-informed neural networks (PINNs), integrating advantages of neural networks with physics-based knowledge, is extended to predict the spatiotemporal patterns of phase-separated microstructures of multicomponent polymers. It is demonstrated that the PINN-based data-driven model can achieve the forward, backward and bidirectional predictions of spatiotemporally phase-separated patterns of homopolymer blends. All predicted patterns reveal a high accuracy and robustness of PINN-based data-driven model by leveraging a small amount of training data. Particularly, the PINN-based method can be harnessed to infer the latent physical law about the growth kinetics of phase-separated microstructures. In addition, PINN-based data-driven model can also be generalized to forecast the spatiotemporal patterns of microphase-separated nanostructures of block copolymers and infer their growth kinetics. Our study opens the possibility of learning non-equilibrium phenomena of complex polymer systems from only a few snapshots of microstructural evolution.

Analisis Pemahaman Konsep Hukum Kekekalan Energi Mekanik Mahasiswa Tadris IPA Pada Mata Kuliah Energi Dalam Sistem Kehidupan

Physics is a branch of science that contains complex explanations regarding the relationships between various natural events which are expressed as facts, theories, concepts, principles and physical laws. Energy is one of essential chapter in physics which contains concepts that need to be understood not memorized. One of the topics discussed in energy material which includes many abstract concepts is the law of conservation of mechanical energy. Studying the law of conservation of mechanical energy, which contains abstract concepts, certainly requires conceptual understanding not just remembering and memorizing. Understanding concepts in physics is very important because it can help develop critical thinking skill, creative thinking skill, and good problem solving skill. The descriptive quantitative method with a test approach was used as a method in this study and the sample is 72 Tadris IPA college students of IAIN Ponorogo in the 3rd semester. The results of the study show that in the category of understanding the concept of the law of conservation of energy in objects moving vertically upwards was classified as moderate, in the category understanding the concept of the law of conservation of energy in objects moving along a certain path was classified as low.