Physical Laws Research Articles

Physical reservoir computing: a tutorial

AbstractThis 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

AbstractIn 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.

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].

The pKa of Water and the Fundamental Laws Describing Solution Equilibria: An Appeal for a Consistent Thermodynamic Pedagogy

AbstractA recurring misconception in some textbooks and research papers has led to an abandonment of fundamental physical laws when describing a subset of acid‐base equilibria, especially regarding the role of the solvent. In the specific case of the autoprotolysis of water, experiments and theoretical calculations prove that the Kw of water at 25 °C, 1.00×10−14, is identical to its acid ionization constant, Ka. Nevertheless, several articles have been published erroneously purporting to give theoretical proof that the Ka of water is 10−15.743 (1.81×10−16) and that the Ka of the aqueous proton (hydronium ion) is 55.3 rather than 1.00. Here we argue that using the incorrect numbers has pedagogical implications beyond those of a simple error. Arguments for the incorrect Ka and pKa values require a misapplication of Henry's law and violate long‐standing methods that use Raoult's law and the conservation of matter to describe the behavior of solutions. As a result, chemistry students may be asked to accept one set of physical principles in one course and another set in another course. Here we argue for adherence to fundamental physical laws governing solution equilibria as applied to the autoprotolysis of water and all aqueous acid base equilibria.

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).

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

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.

Do regular quantum black holes exist?

Regular black holes do not exist in any classical theory of gravity including Einstein's general relativity. This unappealing feature is due to the appearance of a singularity in the interior of the black hole described by any classical theory. As Hawking argued, all known laws of physics must break down at the singularity. It is thus an important question whether this singularity can disappear in a quantum mechanical description of spacetime. In this letter, we therefore quantize the black hole interior in a Kantowski-Sachs minisuperspace representation in the presence of spontaneous Klein-Gordon matter field fluctuations. This leads to a Wheeler-DeWitt equation whose solution yields the interior wave function of the black hole. The regular part of this wave function satisfies the DeWitt boundary condition in that it vanishes at the singularity. Moreover, the wave function is regular and well behaved in the region around the singularity. These features of the wave function suggest that regular black holes do exist in quantum gravity.

A gas that breaks the laws of physics

A gas that breaks the laws of physics

Physics-informed line graph neural network for power flow calculation.

Power flow calculation plays a significant role in the operation and planning of modern power systems. Traditional numerical calculation methods have good interpretability but high time complexity. They are unable to cope with increasing amounts of data in power systems; therefore, many machine learning based methods have been proposed for more efficient power flow calculation. Despite the good performance of these methods in terms of computation speed, they often overlook the importance of transmission lines and do not fully consider the physical mechanisms in the power systems, thereby weakening the prediction accuracy of power flow. Given the importance of the transmission lines as well as to comprehensively consider their mutual influence, we shift our focus from bus adjacency relationships to transmission line adjacency relationships and propose a physics-informed line graph neural network framework. This framework propagates information between buses and transmission lines by introducing the concepts of the incidence matrix and the line graph matrix. Based on the mechanics of the power flow equations, we further design a loss function by integrating physical information to ensure that the output results of the model satisfy the laws of physics and have better interpretability. Experimental results on different power grid datasets and different scenarios demonstrate the accuracy of our proposed model.

DeepPipe: A Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network for Hydraulic Transient Simulation of Multi-Product Pipeline

In the chemical pipelining industry, owing to the high-pressure transportation process, an accurate hydraulic transient simulation tool plays a central role in preventing the slack line flow and overpressure from causing pipeline operation treacherous. Nevertheless, the current model-driven method often faces challenges in balancing computational efficiency with accuracy, and the existing data-driven models struggle to produce explainable results from the physics perspectives since insufficient theoretical principles are incorporated into the model training. Additionally, the existing physics-informed learning architecture fails to achieve a gradient-balanced training, resulting from the significant magnitude difference in outputs and multiple loss terms. Consequently, a Multi-Stage Knowledge-Enhanced Physics-Informed Neural Network (MS-KE-PINN) is proposed for the hydraulic transient simulation of multi-product pipelines. To enforce the neural network producing simulation results with high consistency to physical laws, the governing equations, boundary, and initial condition are incorporated into the training process for an efficient mesh-free simulation. Then, considering that the significant magnitude difference between outputs can easily lead to deficient performance in the gradient descent, the magnitude conversion on the outputs and the equivalent conversion of the governing equations are implemented to enhance the training effect of the neural network. Subsequently, to tackle the imbalanced gradient of multiple loss terms with fixed weights, a multi-stage hierarchical training strategy is designed to improve the approximation capacity of the neural network. Numerical simulation cases demonstrate a better approximation function of the proposed model than the state-of-art models, while the mean absolute percentage errors yielded by MS-KE-PINN are reduced by 77.4 %, 88.7 %, and 87.8 % in three simulation operation conditions for pressure prediction. Furthermore, experimental investigations from a real-world multi-product pipeline suggest that the proposed model can still draw accurate simulation results even under complex and dynamic hydraulic transient scenarios in practice, with root mean squared errors reduced by 94.8 % and 80 % than that of the physics-informed neural network. To this end, the proposed model can conduct accurate and effective hydraulic transient analysis, thus ensuring the safe operation of the pipeline.

Research on the Correlation between Quantum Entanglement and Thinking Consciousness

The development of quantum technology has profoundly influenced research on consciousness. By analyzing the interaction of various microscopic particles in the universe, the article proposed that all things exhibit interconnected non-locality in both time and space, and that the interrelated connections of everything in the universe are based on quantum entanglement. Through analysis of the cellular and nervous system functions of organisms, as well as the influence of quantum science theories on consciousness and combined with research results in related fields, this paper presents the following viewpoints: first, life bodies exhibit quantum entanglement phenomena. Second, biological electricity phenomena are closely tied to consciousness. Third, both life bodies and non-life bodies are conscious, but different entities exhibit varying degrees of strong and weak consciousness. The strong and weak thinking consciousness influences the fluctuation of quantum and the size of quantum entanglement degree. Quantum fluctuations affect the consciousness of both life bodies and non-life bodies, and the entanglement quantum influences the degree of inductance between each other. Finally, the combination of quantum entanglement technology and thinking consciousness can realize the interconnection between human beings and the universe. Consciousness may affect the results measured in quantum mechanics when people observe. However, the macroscopic world mainly adopts the laws of classical physics to solve problems in life. Under certain conditions, the law of quantum physics and the law of classical physics can affect people’s lives together. No matter what angle we view the material world, it is real, and the universe is not illusory.

Assessment of the degree of ripeness of oriental persimmon fruits (Diospyros kaki L.) by tensor stress

The object of the study is persimmon fruits, which occupy an important place among subtropical crops and have a wide development prospect. Since these fruits are difficult to process, they are mainly used fresh. The usefulness of these fruits is associated with their chemical composition. This composition includes biologically active substances, microelements, various mono- and polysaccharides, saturated and unsaturated fatty acids, etc. The complexity of the technological processing of persimmon fruits is associated with its astringent taste, which is determined by the amount of polyphenolic compounds. In general, the strength of raw materials is manifested not only in the degree of ripeness, but also in its technological processing processes, which are the object of the study. From this point of view, the hardness of persimmon fruits acts as a subject of study. Data on the property of fruits and vegetables associated with the stress-hard state is a solution to the problems that arise when expanding the range of finished products. For example, it has been established that at the stage of commercial ripeness, the hardness of persimmon fruits is no more than 12.3 kg/cm2. And this indicator changes downwards over time, i.e. to 1.5÷2.0 kg/cm2. Consequently, the possibility of using fruits for the production of various food products is expanding. The study of raw materials according to the laws of solid state physics is explained by its polymer structure. Therefore, the ripening of raw materials depends on the monomerization of this structure. In such decomposition, a condition is created for the combination of various mono-substances, for example, in persimmon fruits, monophenols combine with monosaccharides, which results in a decrease or disappearance of the tart taste of the raw material. Therefore, determining the degree of raw material ripeness by changes in stress will allow to predict its destruction in advance.

The Jocaxian’s nothingness

This article explains a theory about the origin of the laws of physics and the Universe.

Interpreting Arc and Line Shapes in the Fault Ruptures of the 2016 Mw7.8 Kaikoura, New Zealand and the 2023 Mw7.8 and Mw7.6 East Anatolian Fault, Turkey-Syria Earthquakes: A Theoretical Approach

This study examined the arc and line shapes produced in the fault ruptures of the 2016 Mw7.8 Kaikoura earthquake in New Zealand and the 2023 Mw7.8 and Mw7.6 East Anatolian Fault earthquakes in Turkey-Syria. Theoretical fault mechanisms and physical laws of movement were used to interpret the conceptual geometry of the arc and line shapes, and kinematics force movement. Using computer-aided design (CAD) on the Universal Transverse Mercator (UTM) metric projection, this paper presents earthquake parameters defining the fault geometry, including straight lines and arc shapes with specific measurements such as radius, length, angles, and normal/perpendicular vectors. Comparative analysis revealed distinctions between the two seismic events. Specifically, the Kaikoura earthquake exhibited a smaller normal vector compared to the Turkey-Syria earthquakes. Further interpretation uncovered that the Kaikoura earthquake resulted from pressure exerted by the radius arc vector from both the south-east and north-west, aligning with the continuation of the north-easternmost fault rupture. This suggests that the primary fault vector aligns with the fault trend. In contrast, the Turkey-Syria earthquakes displayed two independent circuit systems. The first event in the Turkiye-Syria rupture underwent an orientation change or bending of about 137 degrees (from N24oE to N68oE). The normal vector of the second earthquake originated from the bending angle of the first earthquake, close to its hypocenter. The rupture of the Kaikoura earthquake followed a lineament orientation of N47oE, forming an approximately 10-km wide corridor, comprising both straight lines and arc shapes.

Exploring the theoretical minimum energy use in the U.S. office building sector

This paper explores the minimum energy that would be required to provide all energy-using services currently used the U.S. office building sector if they were all provided by thermodynamically ideal devices that do not violate physical law. Data from the U.S. DOE and the General Services Administration were used to identify office building workspace services, equipment, occupancy and schedules. Internal building loads were estimated after identifying or conservatively approximating the thermodynamically ideal power required for each service. The average thermodynamically ideal office building in the U.S. would use 2.2 kWh/m2-yr or less, which represents 1.0% of the current average end-use consumption in the sector. Such a large gap between the thermodynamic limit and today’s practice is related to the fact that the energy performance of all the internal gain producing equipment in buildings is far from ideal along with the HVAC equipment and envelope components. Ideal office buildings would use 69.7% of their energy for cooking and related domestic water heating and cooling; illumination accounts for a smaller portion, consuming 17.5%, while office equipment and cooling would use 6.9% and 5.9% of the total consumption, respectively. Dramatic reductions come from the elimination of heating energy and air distribution energy brought about by ideal adiabatic building envelopes and frictionless air distribution. Cooling energy is virtually eliminated by the combination of greatly reduced cooling loads and ideal Carnot equipment. But the thermodynamically ideal office equipment would use two orders of magnitude less energy than today as well.

The Possible Factors that Influence the Stability of Binary Star System

Binary star systems are a crucial subject in astrophysics, as they provide insights into stellar formation, evolution, and dynamics. Despite extensive research, there remains a gap in understanding the factors influencing the longterm stability of such systems. This paper discusses the factors that may affect the stability of binary star systems by analyzing and exploring the physical laws and formulas of the operation of binary star systems and using the operation data of specific binary star systems as evidence. It can be concluded that the factors affecting the stability of a binary star system may be as follows: 1. Mass ratio of two stars, 2. Stellar spacing, 3. Orbital eccentricity, 4. Influence of stellar evolution stage, 5. The influence of external bodies. Future research should further explore the combined effects of these factors in more complex systems to better understand binary star stability in various astrophysical environments. Additionally, understanding how these factors interact in multi-body systems could provide deeper insights into the evolution of complex stellar systems.