Physical Information Research Articles

Physical health mindsets and information avoidance

Health mindsets refer to beliefs about the malleability (growth mindset) versus stability (fixed mindset) of physical health and have gained traction as a predictor of health beliefs and behaviors. Across two studies, we tested whether health mindsets were associated with avoiding personalized health risk information. In Study 2, we also tested whether conceptually-related constructs of internal and chance health locus of control, health self-efficacy, fatalism, and genetic determinism were associated with information avoidance. Health mindsets were manipulated in Study 1 (college students, n = 284; 79.58% female; Mage = 19.74) and measured in Study 2 (participants recruited through MTurk, n = 735; 42.04% female; Mage = 35.78). In both studies, participants viewed a prediabetes infographic and were informed they could learn their prediabetes risk by completing an online risk calculator. Behavioral obligation was also manipulated in both studies to test whether an additional behavioral requirement associated with learning one’s risk would exacerbate any negative impact of health mindsets on avoidance rates. All participants then indicated their interest in learning their prediabetes risk (avoidance intentions) and decided whether to complete the online risk calculator (avoidance behavior). In Study 1, there was no impact of health mindsets, behavioral obligation, or their interaction on avoidance intentions or behavior. Study 2 similarly did not provide consistent evidence for an association of health mindsets, behavioral obligation, or their interaction with avoidance intentions or behavior. However, in Study 2, internal health locus of control was consistently associated with both intentions and behavior. Health information avoidance may be a barrier to prevention and early detection of disease. To encourage individuals to learn potentially important health information, public health interventions might seek to increase people’s beliefs that their own actions play a role in their health outcomes. Interventions may also seek to increase people’s knowledge about and skills regarding improving their health outcomes, which may influence health locus of control beliefs.

Physical extraction of antigen and information

To respond and adapt, cells use surface receptors to sense environmental cues. While biochemical signal processing inside the cell is studied in depth, less is known about how physical processes during cell-cell contact impact signal acquisition. New experiments found that fast-evolving immune B cells in germinal centers (GCs) apply force to acquire antigen clusters prior to internalization, suggesting adaptive benefits of physical information extraction. We present a theory of stochastic antigen transfer and show that maximizing information gain via physical extraction can explain the dramatic phenotypic transition from naive to GC B cells-attenuated receptor signaling, enhanced force usage, and decentralized contact architecture. Our model suggests that binding-lifetime measurement and physical extraction serve as complementary modes of antigen recognition, greatly extending the dynamic range of affinity discrimination when combined. This physical-information framework further predicts that the optimal size of receptor clusters decreases as affinity improves, rationalizing the use of a multifocal synaptic pattern seen in GC B cells. By linking extraction dynamics to selection fidelity via discriminatory performance, we propose that cells may physically enhance information acquisition to sustain adaptive evolution.

Variational approach to learning photonic unitary operators

Structured light, light tailored in its internal degrees of freedom, has become topical in numerous quantum and classical information processing protocols. In this work, we harness the high dimensional nature of structured light modulated in the transverse spatial degree of freedom to realize an adaptable scheme for learning unitary operations. Our approach borrows from concepts in variational quantum computing, where a search or optimization problem is mapped onto the task of finding a minimum ground state energy for a given energy/goal function. We achieve this by a pseudo-random walk procedure over the parameter space of the unitary operation, implemented with optical matrix-vector multiplication enacted on arrays of Gaussian modes by exploiting the partial Fourier transforming capabilities of a cylindrical lens in the transverse degree of freedom for the measurement. We outline the concept theoretically, and experimentally demonstrate that we are able to learn optical unitary matrices for dimensions d = 2, 4, 8, and 16 with average fidelities of >90%. Our work advances high dimensional information processing and can be adapted to both process and quantum state tomography of unknown states and channels.

Proposition of Optimization of the Meetinthemiddle Protocol for Quantum Repeaters

Quantum computing relies on concepts derived from classical information theory and quantum physics. In order for quantum communications to be a reality, it is essential to have the most advanced infrastructures in place. In this paper, we explore the significance of quantum communication and the utilization of quantum repeaters, which rely on the phenomenon of quantum entanglement. We present the MeetInTheMiddle protocol for quantum repeaters and its total transmission probability, and then propose an improvement of this protocol based on the comparison of the total transmission probability.

Robust quantitative X-ray phase diagnostic for carbon composite characterisation in the context of lightning induced risk

Getting complementary physical information from a single image acquisition is particularly valuable for materials analysis. Grating based X-ray Phase Contrast Imaging (XPCI) methods allow decoupling attenuation, phase and scattering information. However, the phase and scattering extraction processes can easily suffer from artefacts, which is detrimental to implement this imaging technique in societal applications. In this paper, we demonstrate that grating based XPCI can provide a robust phase measurement in complex materials such as damaged composites. The technique allows the phase to be analysed using a self-assessment method that first identifies the artefacts from the imaging setup, and then can be used as an indicator to interpret the signal from a material. We focus on carbon fibre reinforced polymers which we subjected to laboratory-controlled lightning strikes. We evidence that the combined information from phase and attenuation allow identifying the type of defect induced by the lightning current. The phase information is converted into relative mass density variation within the sample and depicts areas with a loss in density up to 40%. We ensure that these results are valid by comparing them with an X-ray attenuation contrast tomographic reconstruction.

Prediction of the minimum miscibility pressure for CO2 flooding based on a physical information neural network algorithm

Abstract The minimum miscibility pressure (MMP) is a crucial parameter in assessing the miscibility of CO2 displacement and evaluating the effectiveness of oil displacement. Traditional methods for calculating MMP are intricate and time-consuming, involving numerous related parameters. Therefore, precise and efficient determination of MMP is highly significant in the development of CO2-driven reservoirs. This study first utilized the Pearson correlation coefficient to analyse the correlation factor mechanism of 36 sets of fine-tube experimental data. Subsequently, the physical information neural network prediction model was employed with reservoir temperature, crude oil composition, and injected gas type as input parameters. The PRI state equation and Glaso correlation equation drove the model, with parameter optimization and training conducted under both physical and data driving. The model demonstrates high prediction accuracy and strong generalization ability. Finally, Validation of the model was performed using fine-tube experimental data from 5 other wells, revealing a relatively small relative deviation between calculated and experimental values, with an average coefficient of determination of 0.95 and an average relative error of 4.42%. The prediction accuracy was improved by about 75% compared to other machine learning algorithms. This model holds potential for application in on-site reservoir development, enhancing the measurement accuracy of the minimum miscible pressure of pure CO2 flooding, greatly shortening the design cycle of reservoir development, expediting the process of reservoir development, and providing technical guidance for improving oil and gas recovery and pure CO2 flooding exploration and development.

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

Pengambilan Keputusan Bercerai Tokoh Kinan Pada Web series Layangan Putus Ditinjau Dari Psikologi Dan Hukum Islam

Abstract: The research aims to find out how the decision to divorce the character Kinan in the web series Kite Putus is viewed from psychology and Islamic law. The research method uses a qualitative approach in the study of Cyberliterary Psychology. The research was carried out by watching the web series to be analyzed descriptively and inductively, then created in the form of text and analyzed. Descriptive analysis describes the aspects and characteristics of the message, while inductive analysis is used to understand and examine the meaning. The data source was obtained from the rater through a questionnaire that met the gender requirements of female, Islamic religious education student and early adulthood. Next, the data was validated through an inter-rater test. Kinan's character's psychological decision-making process goes through five stages. First, assess the challenges stemming from the phenomenon of changes in her husband's attitude which gives rise to confusion and suspicion, then encourage her to collect data and information to find the reasons for the change in her husband's attitude. Second, an alternative survey regarding the findings of physical evidence and information originating from Kinan's friends and people around Aris, which leads to proof of her husband's infidelity. Third, consider alternatives by carrying out a negative evaluation of the loss of hope of forming a harmonious family and the opportunity to live a new life while maintaining one's self-esteem. Fourth, declare your commitment, express your stance by suing Aris in court while continuing to consult with friends who are competent in the legal field. Also, keep sharing with friends who can provide in-depth insight into the correct attitude in dealing with developments in family problems. Fifth, survive negative feedback, face her husband's negative attitude by continuing to manage negative emotions and focus on gathering evidence that clarifies her husband's commitment to polygamy. Meanwhile, the Kinan character's decision to divorce in terms of Islamic law is permissible because it meets the requirements for being included in the Fasakh case or not. can be corrected again because Aris' behavior violates Islamic religious law as stated in the Al-Quran surah An-Nur verses 30-31. The problems include infidelity and quarrels which cause her to not be able to get back together with her husband. Keywords: Cybersastra, Decision making, Islamic law, Psychology, Web series.

A chemo-mechanical model for growth and mechanosensing of focal adhesion

Focal adhesion (FA), the complex molecular assembly across the lipid membrane, serves as a hub for physical and chemical information exchange between cells and their microenvironment. Interestingly, studies have shown that FAs can grow along the direction of contractile forces generated by actomyosin stress fibers and achieve larger sizes on stiffer substrates. In addition, the cellular traction transmitted to the substrate was observed to reach the maximum near the FA center. However, the biomechanical mechanisms behind these intriguing findings remain unclear. To answer this important question, here we first developed a one-dimensional (1D) chemo-mechanical model of FA where key features like adhesion plaque deformation, active contraction by stress fibers, force-dependent association/dissociation of integrin bonds connecting two surfaces, and substrate compliance have all been considered. Within this formulation, we showed that the rigidity-sensing capability of FAs originates from the deformability of stress fibers while the force-dependent breakage of integrin bonds leads to the appearance of the traction peak at the FA center. Furthermore, by extending the model into three-dimensional as well as incorporating assembly/dis-assembly kinetics of adhesion proteins, we also demonstrated how anisotropic stress/strain field within the adhesion plaque will be induced by the presence of contractile forces which leads to the directional growth of the FA.

Exploring the Feasibility of Digital Voice Assistants for Delivery of a Home-Based Exercise Intervention in Older Adults With Obesity and Type 2 Diabetes Mellitus: Randomized Controlled Trial.

Current clinical guidelines for the management of type 2 diabetes mellitus (T2DM) in older adults recommend the use of antihyperglycemic medications, monitoring of blood glucose levels, regular exercise, and a healthy diet to improve glycemic control and reduce associated comorbidities. However, adherence to traditional exercise programs is poor (<35%). Common barriers to adherence include fear of hypoglycemia and the need for blood glucose level monitoring before exercise. Digital health strategies offer great promise for managing T2DM as they facilitate patient-practitioner communication, support self-management, and improve access to health care services for underserved populations. We have developed a novel web-based software program allowing practitioners to create tailored interventions and deliver them to patients via digital voice assistants (DVAs) in their own homes. We aim to evaluate the feasibility of a 12-week, home-based, personalized lifestyle intervention delivered and monitored by DVAs for older adults with obesity and T2DM. In total, 50 older adults with obesity aged 50-75 years with oral hypoglycemic agent-treated T2DM were randomized to the intervention (DVA, n=25) or a control group (n=25). Participants allocated to the DVA group were prescribed a home-based muscle strengthening exercise program (~20- to 30-min sessions) and healthy eating intervention, delivered via DVAs (Alexa Echo Show 8; Amazon) using newly developed software ("Buddy Link"; Great Australian Pty Ltd). Control group participants received generalized physical activity information via email. Outcomes were feasibility, DVA usability (System Usability Scale), and objectively assessed physical activity and sedentary time (wrist-worn accelerometers). In total, 45 (90%) out of 50 participants completed this study. Mean adherence to prescribed exercise was 85% (SD 43%) with no intervention-related adverse events. System usability was rated above average (70.4, SD 16.9 out of 100). Compared with controls, the DVA group significantly decreased sedentary time (mean difference -67, SD 23; 95% CI -113 to -21 min/d), which was represented by a medium to large effect size (d=-0.6). A home-based lifestyle intervention delivered and monitored by health professionals using DVAs was feasible for reducing sedentary behavior and increasing moderate-intensity activity in older adults with obesity and T2DM. Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12621000307808; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381364&isReview=true.

Vertex-based graph neural network classification model considering structural topological features for structural optimization

Traditional surrogate models always face the challenge of low accuracy when dealing with high-dimensional problems in structural optimization, this study aims to overcome this problem and proposes a vertex-based graph neural network (GNN) classification model. In contrast to conventional machine learning models that treat design variables as independent inputs, the proposed model develops a vertex-based graph representation to transform structural topological features and critical physical information into the graph data. According to a message passing mechanism based on the graph convolutional, it can extract the correlations among design variables and enhance its capability in handling high-dimensional structural optimization problems. Three truss examples, including a 10-bar with 10 variables, a 600-bar with 25 variables, and a 942-bar with 59 variables, are utilized to investigate the performance of the proposed surrogate model. The results demonstrate that the GNN-based surrogate model outperforms traditional machine learning approaches, particularly in the two high-dimensional problems, showcasing its superior ability to capture complex variable correlations and handle high-dimensional structural optimization tasks. Moreover, the proposed method significantly reduces the computational expenses by over 60% compared to conventional metaheuristic algorithms, while yielding optimal designs with comparable quality. These results demonstrate the efficiency and effectiveness of the GNN-based surrogate model in tackling complex, high-dimensional structural optimization problems.

A microfluidic patch for wireless wearable electrochemical detection of sweat metabolites

Wearable sweat sensors provide an innovative detection method for analyzing human physiological and biochemical parameters and play an important role in detecting the potential risks of chronic diseases. Existing commercial wearable sensors (Apple iWatch, HUAWEI smart band) have problems such as single detection index (heart rate or blood pressure), and mainly focus on the physical information. This work studied an integrated microfluidic patch to simultaneously detect several biomarkers (pH, sodium ions, and uric acid) in sweat. We achieved the detection of pH level, sodium, and uric acid in linear ranges of 4–8, 0–160 mM, and 5–160 μM, respectively, covering well the clinically relevant ranges in the healthy human sweat. Laser engraving was chosen to prepare a microfluidic layer, and the sweat collection function was realized by combining hydrophilic and hydrophobic microfluidic synergy. It could efficiently collect the required sweat sample volume (80 μL) within 30 s while avoiding the contamination caused by directly contact with the skin. In addition, a wireless signal acquisition and transmission system was designed with the signal reading, processing, and wireless transmission functions, which can display the testing results in real time on Android phone-specific application software. We combined the wireless system with a microfluidic patch for on-body testing to verify the good wearability of the sweat sensor. This research realized the wireless data processing and transmission while improving sweat collection efficiency. In addition, it could detect various sweat physiological components in real-time, and provides a multiparameter, real-time, and portable sensing platform.

A Novel Authenticated Quantum Anonymous Secret Sharing for Classical and Quantum Information

AbstractAnonymous secret sharing (ASS) is an essential cryptographic concept that facilitates the sharing and reconstruction of secret information while safeguarding the identity of the involved secret receivers, which has broad applications in key management, data backup, and distributed systems. In this study, a novel authenticated quantum anonymous secret sharing (QASS) protocol that emphasizes information privacy and identity anonymity protection is proposed. Employing ‐level multipartite GHZ states as a quantum resource, one‐sided anonymous entanglement (AE) is innovatively established between the dealer and anonymous receivers, enabling the dealer to distribute a random share of secret information. Additionally, by establishing a one‐sided AE between anonymous receivers and restorer, the restorer can securely collect and reconstruct the secret information using quantum teleportation (QT). Rigorous security analysis demonstrates that protocol can resist attacks from active adversaries and potentially dishonest users. Quantum experiments on IBM Qiskit validate the correctness and feasibility of the proposed QASS protocol. This work contributes to the advancement of quantum anonymous communication, addressing the requirements for information privacy and identity anonymity in practical application environments.

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

Method of Digital Processing of Optical Spectra of Magnetron Discharge Plasma

To solve the actual problems associated with the development of the theory of magnetron discharge and the expansion of its practical application, a digital method of recording and processing the discharge plasma luminescence spectra is proposed in this work. To obtain the discharge plasma glow spectra, a photographic technique was used, which allowed simultaneous recording of the entire radiation spectrum in the 390.0-700.0 nm region. An additional advantage of this technique is the ability to track spatial changes in the composition and properties of the plasma in the discharge in the selected direction. A Canon EOS 80D digital camera with remote control was used to record the optical signal. A graphical application OSA was created to process digital images of the discharge plasma luminescence spectra. The paper describes the functionality of this application: determination of the wavelength of a spectral line and its belonging to a certain chemical element; measurement of the spatial distribution of the intensity of a spectral line along the selected direction of radiation registration. Determining the wavelength of a spectral line in the application is possible in two modes of operation - automatic and manual. In the first mode, the algorithm developed in this paper determines the wavelength for all spectral lines whose intensity exceeds the background value at a height of 10 % of the lower spectral limit. The second mode allows you to independently select a single spectral line or several to determine their wavelengths. The first mode is used for quick analysis, while the second mode allows you to determine the length of the spectral line with greater accuracy. To interpret the spectral lines, the methodology of reference lines from the databases of spectral line tables for various elements is used. The possibility of both full automatic verification, where all elements are sequentially searched, and selective verification, where one or more elements are selected, is provided. The paper shows that the spatial distribution of the intensity of tungsten spectral lines, and thus of excited atoms in a magnetron discharge, is a complex function of the distance from the cathode, which depends on the discharge parameters. The proposed digital methodology makes it possible to significantly speed up the process of obtaining physical information and increase the accuracy in determining the spectrum parameters.

Dimensionality reduction in bulk-boundary reaction-diffusion systems.

Intracellular protein patterns regulate many vital cellular functions, such as the processing of spatiotemporal information or the control of shape deformations. To do so, pattern-forming systems can be sensitive to the cell geometry by means of coupling the protein dynamics on the cell membrane to dynamics in the cytosol. Recent studies demonstrated that modeling the cytosolic dynamics in terms of an averaged protein pool disregards possibly crucial aspects of the pattern formation, most importantly concentration gradients normal to the membrane. At the same time, the coupling of two domains (surface and volume) with different dimensions renders many standard tools for the numerical analysis of self-organizing systems inefficient. Here, we present a generic framework for projecting the cytosolic dynamics onto the lower-dimensional surface that respects the influence of cytosolic concentration gradients in static and evolving geometries. This method uses a priori physical information about the system to approximate the cytosolic dynamics by a small number of dominant characteristic concentration profiles (basis), akin to basis transformations of finite element methods. As a proof of concept, we apply our framework to a toy model for volume-dependent interrupted coarsening, evaluate the accuracy of the results for various basis choices, and discuss the optimal basis choice for biologically relevant systems. Our analysis presents an efficient yet accurate method for analyzing pattern formation with surface-volume coupling in evolving geometries.

Masked pretraining strategy for neural potentials

We propose a masked pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water and small organic molecule systems. GNNs are pretrained by recovering the spatial information of masked-out atoms from molecules selected with certain ratios and then transferred and fine-tuned on atomic force fields. Through such pretraining, GNNs learn meaningful prior about the structural and underlying physical information of molecule systems that are useful for downstream tasks. With comprehensive experiments and ablation studies, we show that the proposed method improves both the accuracy and convergence speed of GNNs compared to their counterparts trained from scratch or with other pretraining techniques. This approach showcases its potential to enhance the performance and data efficiency of GNNs in fitting molecular force fields.

Development of backward compatible physics-informed neural networks to reduce error accumulation based on a nested framework

Physical information neural network (PINN) provides an effective method for solving partial differential equations, and many variants have been derived, the most representative of which is backward compatible physical information neural network (BC-PINN). The core of BC-PINN is to use the prediction of the previous time period as the label data of the current time period, which leads to error accumulation in the process of backward compatibility. To solve this problem, a nested backward compatible physical information neural network (NBC-PINN) is proposed in this paper. NBC-PINN has an overlap region between the computation domain of the previous time period and the computation domain of the current time period, which is trained twice in total. Numerical experiments on four representative time-varying partial differential equations show that NBC-PINN can effectively reduce error accumulation, improve computational efficiency and accuracy, and improve the L2 relative error of the numerical solution with fewer residual allocation points. The development of NBC-PINN provides a theoretical basis for the scientific calculation of partial differential equations, and promotes the progress of PINN to a certain extent.

An analytic traction-displacement model for a reinforcing ligament bridging a crack at an arbitrary angle, including elastic, frictional, snubbing, yielding, creep, and fatigue phenomena

A micromechanical model is developed that generates analytic expressions for the crack displacement vector u given an arbitrary far-field stress state σa for a crack that is bridged by an array of ligaments oriented at an arbitrary angle with respect to the crack plane. The model is applicable to various materials, e.g., fibrous ceramic composites, or polymer composites reinforced by stitches or z-pins or woven tows, and deals with interfacial friction, enhanced friction due to increased contact pressure (“snubbing”), and the possibility of ligament deflection enabled by yield or damage. The model also conveniently incorporates ligament failure and rate dependent phenomena (fatigue or creep). Adaptability of the model is enabled by the definition of a standard Reference Model, which generates analytic expressions for the crack displacement for given possible yield, ligament deflection, and friction and snubbing effects and is invariant for all geometrical and material choices. The switching on or off and the strengths of all phenomena are governed by assigning values to a handful of material parameters. The material parameters will generally be calibrated against data in a top-down strategy, the model thereby mapping material selection onto engineering fracture via the predicted bridging relationship u[σa]. The relationship u[σa] can depend strongly on bi-angular ligament orientation. Yield and deflection can change u[σa] qualitatively, e.g., by creating fracture surface contact even when σa includes substantial opening tension. Snubbing has significant effects, including possible stabilization of the pullout of a finite ligament. Since model output is computed via analytic expressions, its speed will support the model's use in large-scale material simulations or as constraining physical information in machine learning algorithms.

Review of Recent Additive Manufacturing and Welding Research with Application of Physics-Informed Neural Networks

This review introduces recent research on applying physics-informed neural networks (PINNs) to additive manufacturing and welding. PINNs, which are artificial intelligence models, integrate governing equations containing physical information with artificial neural networks, enabling the modeling of complex physical phenomena at a lower computational cost than traditional numerical models. Although PINNs have been employed in a limited number of studies on welding processes, they have been extensively used in various studies within the field of additive manufacturing. This study reviews the theoretical background of PINNs to explore their effective application to welding processes, examining 12 research cases in additive manufacturing and two research cases in welding processes. The analysis included the structure of the PINN, governing equations, and prediction results of each study. Results indicate that PINNs provide faster computation speeds and higher prediction accuracies than numerical models. Moreover, they could perform analyses without additional training even when process parameters and materials changed. Additionally, PINNs have been effectively applied to predict the mechanical properties of the molten zone. Consequently, PINNs are anticipated to be actively applied in future research on welding process modeling and mechanical property prediction.