Physical Principles Research Articles

Towards Realistic Human Motion Prediction with Latent Diffusion and Physics-Based Models

Many applications benefit from the prediction of 3D human motion based on past observations, e.g., human–computer interactions, autonomous driving. However, while existing methods based on encoding–decoding achieve good performance, prediction in the range of seconds still suffers from errors and motion switching scarcity. In this paper, we propose a Latent Diffusion and Physical Principles Model (LDPM) to achieve accurate human motion prediction. Our framework performs human motion prediction by learning information about the potential space, noise-generated motion, and combining physical control of body motion, where physics principles estimate the next frame through the Euler–Lagrange equation. The framework effectively accomplishes motion switching and reduces the error accumulated over time. The proposed architecture is evaluated on three challenging datasets: Human3.6M (Human 3D Motion Capture Dataset), HumanEva-I (Human Evaluation dataset I), and AMASS (Archive of Motion Capture as Surface Shapes). We experimentally demonstrate the significant superiority of the proposed framework in the prediction range of seconds.

OAH-Net: a deep neural network for efficient and robust hologram reconstruction for off-axis digital holographic microscopy

Off-axis digital holographic microscopy is a high-throughput, label-free imaging technology that provides three-dimensional, high-resolution information about samples, which is particularly useful in large-scale cellular imaging. However, the hologram reconstruction process poses a significant bottleneck for timely data analysis. To address this challenge, we propose a novel reconstruction approach that integrates deep learning with the physical principles of off-axis holography. We initialized part of the network weights based on the physical principle and then fine-tuned them via supersized learning. Our off-axis hologram network (OAH-Net) retrieves phase and amplitude images with errors that fall within the measurement error range attributable to hardware, and its reconstruction speed significantly surpasses the microscope’s acquisition rate. Crucially, OAH-Net, trained and validated on diluted whole blood samples, demonstrates remarkable external generalization capabilities on unseen samples with distinct patterns. Additionally, it can be seamlessly integrated with other models for downstream tasks, enabling end-to-end real-time hologram analysis. This capability further expands off-axis holography’s applications in both biological and medical studies.

Sea Surface Temperature Forecasting Using Foundational Models: A Novel Approach Assessed in the Caribbean Sea

Sea surface temperature (SST) plays a pivotal role in air–sea interactions, with implications for climate, weather, and marine ecosystems, particularly in regions like the Caribbean Sea, where upwelling and dynamic oceanographic processes significantly influence biodiversity and fisheries. This study evaluates the performance of foundational models, Chronos and Lag-Llama, in forecasting SST using 22 years (2002–2023) of high-resolution satellite-derived and in situ data. The Chronos model, leveraging zero-shot learning and tokenization methods, consistently outperformed Lag-Llama across all forecast horizons, demonstrating lower errors and greater stability, especially in regions of moderate SST variability. The Chronos model’s ability to forecast extreme upwelling events is assessed, and a description of such events is presented for two regions in the southern Caribbean upwelling system. The Chronos forecast resembles SST variability in upwelling regions for forecast horizons of up to 7 days, providing reliable short-term predictions. Beyond this, the model exhibits increased bias and error, particularly in regions with strong SST gradients and high variability associated with coastal upwelling processes. The findings highlight the advantages of foundational models, including reduced computational demands and adaptability across diverse tasks, while also underscoring their limitations in regions with complex physical oceanographic phenomena. This study establishes a benchmark for SST forecasting using foundational models and emphasizes the need for hybrid approaches integrating physical principles to improve accuracy in dynamic and ecologically critical regions.

Mechanical and semantic knowledge mediate the implicit understanding of the physical world.

Most recent accounts highlight the importance of two aspects of cognition in the implicit understanding of the physical world: semantic knowledge (the ability to recognize, categorize, and relate concepts) and mechanical knowledge (the capability to comprehend how things mechanically work). However, how the human brain may integrate these cognitive processes remains largely unexplored. Here, we use functional magnetic resonance imaging to investigate this integration employing a novel free-viewing task. Participants viewed images depicting object-tool pairs that were either mechanically consistent (e.g., nail - steel hammer) or mechanically inconsistent (e.g., scarf - steel hammer). These pairs were situated on a metal plate atop a table, with a stripped electrical cable in contact with the plate that could be plugged in or out from the electrical line, rendering the tools either electrified or not. Task-based functional connectivity revealed an interplay among specific left-brain regions - the middle temporal (MTG), inferior frontal (IFG), and supramarginal (SMG) gyri - during the processing of mechanical actions and physics principles, associating the activity of these areas with mechanical knowledge (SMG) and object-related semantic knowledge (MTG). Notably, the IFG was active during both types of processing, suggesting a critical role of this region in multi-modal information integration. These findings support the most recent integrated neurocognitive models of physical understanding, deepening our comprehension of how we make sense of the physical world.

Monitoring Soil Water Content and Measurement Depth of Cosmic-Ray Neutron Sensing in the Tibetan Plateau

Abstract This work aims at evaluating the potential application of two cosmic-ray neutron sensing (CRNS) soil water content (SWC) retrieval methods and discusses the effective measurement depth of CRNS retrieval and vertical weights of different layers in a semihumid alpine meadow on the Tibetan Plateau. In this study, the widely used N0 method and the latest published method derived from physical principles named the universal transport solution (UTS) were used for SWC estimation during the whole nonfrozen season on the Tibetan Plateau. The N0 and UTS methods successfully retrieved the SWC on the Tibetan Plateau, and their variations were generally consistent. The effective measurement depths calculated by the N0 and UTS methods showed similar fluctuation patterns, ranging from 9.7 to 14.5 cm and 17.8 to 20 cm, with mean values of 12.0 and 19.2 cm, respectively. This work verified that the weighting methods of the N0 and UTS models provided credible performance for the effective measurement depths. The N0 method provided measurement depths more close to the surface, and the UTS method yielded slightly deeper averages. The UTS equation had a more sensitive response to periods of low precipitation. Including detailed Ultra Rapid Neutron-Only Simulation (URANOS) Monte Carlo simulations of the full time series, we find specifically an excellent agreement in the summer period. Our analysis suggests that CRNS is a promising innovation for SWC measurement and can be extended in its application at hectometer scales to monitor SWC in the Tibetan meadow ecosystem. Significance Statement The purpose of this study is to better understand if cosmic-ray neutron sensing (CRNS) has the potential to sense soil water content (SWC) for the Tibetan meadow ecosystem with harsh climate conditions. Techniques for measuring SWC have evolved over many decades, from point-scale measurements of a few meters to satellite-based remote sensing methods of several kilometers, each with advantages and disadvantages. This is important because CRNS has emerged to fill the scale gap between point measurements and microwave remote sensing methods. The results are helpful in assessing the applicability of CRNS in alpine meadow ecosystems on the Tibetan Plateau.

Advancements in Surface Acoustic Wave Gyroscope Technology in 2015–2024

Although the theoretical basis for surface acoustic wave gyroscopes (SAWGs) was first proposed in 1980, their design concepts are still under development. Nevertheless, these sensors are of a great interest in the potential market owing to their exceptional shock resistance, small size, low power consumption, and simple manufacturing process that ensures low cost. This paper aims to conscientiously investigate the ideas that have been proposed over the past decade in this area and evaluate the potential development required to bring SAWGs to market. It should be of interest for researchers in the field who might have missed some useful solutions that could be a missing piece in their own design, or for young researchers to inspire their creativity and open new research on the topic. Additionally, since some of the reviewed SAWG design concepts are based on a combination of several physical principles (for example, optical measurements), researchers from other fields may find useful solutions for incorporating surface acoustic wave techniques into their device concepts.

Point-of-care Ultrasound (POCUS) Curriculum for Internists in Turkey: A Position Paper by the DAHUDER Internal Medicine Society Ultrasound Working Group

Background: Point-of-care ultrasound (POCUS) is the application of ultrasound imaging by clinicians at the point of healthcare delivery. POCUS has been used by emergency medicine physicians and intensivists for a long time, but its use in internal medicine is relatively recent. Around the world, there are many position papers, curricula, and training programs regarding POCUS in internal medicine, but there is no standardized curriculum in Turkey. We aimed to set national standards for internists in the POCUS curriculum. Methodology: The DAHUDER Internal Medicine Society Ultrasound Working Group convened members to establish the POCUS Internal Medicine curriculum. We conducted a literature search and informal clinician assessment to create a curriculum that meets the needs, demands, and resources of Turkish internists while also guaranteeing its compatibility with international curricula. Results: We identified ten main domains under the basic and advanced POCUS curriculum as follows: Principles of ultrasound physics, machine basics, thorax imaging, abdominal imaging, cardiac imaging, vascular imaging, thyroid & neck imaging, musculoskeletal imaging, interventional imaging, and approach to clinical scenarios: Protocols. We expect these domains and their content to establish the POCUS imaging standard for internists in Turkey. Conclusion: We expect the POCUS education curriculum to set standards, increase clinicians' skills, and improve patient care as the ultimate outcome.

Models and simulations of structural DNA nanotechnology reveal fundamental principles of self-assembly.

DNA is not only a centrally important molecule in biology: the specificity of bonding that allows it to be the primary information storage medium for life has also allowed it to become one of the most promising materials for designing intricate, self-assembling structures at the nanoscale. While the applications of these structures are both broad and highly promising, the self-assembly process itself has attracted interest not only for the practical applications of designing structures with more efficient assembly pathways, but also due to a desire to understand the principles underlying self-assembling systems more generally, of which DNA-based systems provide intriguing and unique examples. Here, we review the fundamental physical principles that underpin the self-assembly process in the field of DNA nanotechnology, with a specific focus on simulation and modelling and what we can learn from them. In particular, we compare and contrast DNA origami and bricks and briefly outline other approaches, with an overview of concepts such as cooperativity, nucleation and hysteresis; we also explain how nucleation barriers can be controlled and why they can be helpful in ensuring error-free assembly. While high-resolution models may be needed to obtain accurate system-specific properties, often very simple coarse-grained models are sufficient to extract the fundamentals of the underlying physics and can enable us to gain deep insight. By combining experimental and simulation approaches to understand the details of the self-assembly process, we can optimise its yields and fidelity, which may in turn facilitate its use in practical applications.

Research on the Accumulative Damage of Flywheels Due to In-Space Charging Effects

High-speed rotating flywheel bearings, designed for space applications, generate a high-resistance hydrodynamic lubrication film, which isolates the rotor, transforming it into a conductor. This phenomenon introduces a novel failure mode—flywheel bearing electrical damage caused by space charging effects. This paper first reviews the sources of common shaft voltages in flywheels and the mechanisms of electrical damage and improves the principle of deep charge causing shaft voltages in flywheel bearings, proposing that surface charge is another source of shaft voltages. The quantified analysis model of flywheel bearing electrical damage in relation to rotational speed and high-energy electron flux is derived, indicating that the damage caused by space charge–discharge to the bearing is of small magnitude and only becomes apparent after long-term accumulation, thus being easily overlooked. Based on the causal chain of electrical damage, a correlation analysis model consistent with physical principles is constructed, and the correlation between on-orbit anomalies of the flywheel and high-energy electron flux is confirmed through the use of big data. Preliminary experiments are conducted to validate all of the research results. Finally, suggestions are given for the reliable design, application, and testing of flywheels.

PM6-ML: The Synergy of Semiempirical Quantum Chemistry and Machine Learning Transformed into a Practical Computational Method.

Machine learning (ML) methods offer a promising route to the construction of universal molecular potentials with high accuracy and low computational cost. It is becoming evident that integrating physical principles into these models, or utilizing them in a Δ-ML scheme, significantly enhances their robustness and transferability. This paper introduces PM6-ML, a Δ-ML method that synergizes the semiempirical quantum-mechanical (SQM) method PM6 with a state-of-the-art ML potential applied as a universal correction. The method demonstrates superior performance over standalone SQM and ML approaches and covers a broader chemical space than its predecessors. It is scalable to systems with thousands of atoms, which makes it applicable to large biomolecular systems. Extensive benchmarking confirms PM6-ML's accuracy and robustness. Its practical application is facilitated by a direct interface to MOPAC. The code and parameters are available at https://github.com/Honza-R/mopac-ml.

Simulation of sugar beet yield

Mathematical simulation of agricultural crop yields is based on the physical principle of causal interactions balance in a closed physical system. The conditions for model verification are determined, allowing to obtain objective and reliable findings. It is noted that empirical equations representing the dependence of crop yields on crop-forming factors in the form of polynomials of any degree, obtained using the multiple nonlinear regression method, are valid only for the conditions of a specific field experiment. Using them, it is impossible to carry out theoretical generalizations that would allow developing these particular solutions into a generalized mathematical model. It is shown that mathematical model of yield, presented in multiplicative form, can include an unlimited number of yield-forming factors. Atmospheric precipitation was used as a factor characterizing the moisture supply for plants in case of no irrigation. The validity of the developed solution is confirmed by 13 years of data on the yield of sugar beet (NZ-type hybrid) cultivated in Belarus at the State Agricultural Institution “Molodechno Variety Testing Station”. The mathematical model of yield is presented in dimensionless form; all blocks of factors of this model are similarity criteria. This allows to compare the results of mathematical simulation of yield of any agricultural crop on soils with any agrochemical properties. Such an analysis cannot be carried out with the results of calculating yields using the formulas of private mathematical models of agricultural crop yields in the form of algebraic polynomials.

Reliable, efficient, and scalable photonic inverse design empowered by physics-inspired deep learning

Abstract On-chip computing metasystems composed of multilayer metamaterials have the potential to become the next-generation computing hardware endowed with light-speed processing ability and low power consumption but are hindered by current design paradigms. To date, neither numerical nor analytical methods can balance efficiency and accuracy of the design process. To address the issue, a physics-inspired deep learning architecture termed electromagnetic neural network (EMNN) is proposed to enable an efficient, reliable, and flexible paradigm of inverse design. EMNN consists of two parts: EMNN Netlet serves as a local electromagnetic field solver; Huygens–Fresnel Stitch is used for concatenating local predictions. It can make direct, rapid, and accurate predictions of full-wave field based on input fields of arbitrary variations and structures of nonfixed size. With the aid of EMNN, we design computing metasystems that can perform handwritten digit recognition and speech command recognition. EMNN increases the design speed by 17,000 times than that of the analytical model and reduces the modeling error by two orders of magnitude compared to the numerical model. By integrating deep learning techniques with fundamental physical principle, EMNN manifests great interpretability and generalization ability beyond conventional networks. Additionally, it innovates a design paradigm that guarantees both high efficiency and high fidelity. Furthermore, the flexible paradigm can be applicable to the unprecedentedly challenging design of large-scale, high-degree-of-freedom, and functionally complex devices embodied by on-chip optical diffractive networks, so as to further promote the development of computing metasystems.

Extrapolation of cavitation and hydrodynamic pressure in lubricated contacts: a physics-informed neural network approach

A comprehensive understanding of the dynamics of tribological interactions is essential for enhancing efficiency and durability in a multitude of technical domains. Conventional experimental techniques in tribology are frequently costly and time-consuming. In contrast, elastohydrodynamic lubrication (EHL) simulation models present a viable alternative for calculating frictional forces in sealing contacts. These calculations are based on the hydrodynamics within the sealing contact, as defined by the Reynolds equation, the deformation of the seal, and the contact mechanics. However, a significant drawback of these simulations is the time-consuming calculation process. To overcome these experimental and computational limitations, machine learning algorithms offer a promising solution. Physics-informed machine learning (PIML) improves on traditional data-driven models by incorporating physical principles. In particular, physics-informed neural networks (PINNs) are as effective hybrid solvers that combine data-driven and physics-based methods to solve the partial differential equations that drive EHL simulations. By integrating physical laws into the parameter optimization of the neural network (NN), PINNs provide accurate and fast solutions. Thus, unlike traditional NNs, PINNs have the potential to make accurate predictions beyond the limited training domain. The objective of this study is to demonstrate the feasibility of spatial and temporal extrapolation of the PINN and to analyze its reliability, both with and without consideration of cavitation. Two test cases are employed to examine the pressure and cavitation distribution within a sealing contact that extends beyond the spatial and temporal training range. The findings indicate that PINNs can surmount the typical constraints associated with NNs in the extrapolation of solution spaces, which represents a notable advancement in terms of computational efficiency and model flexibility.

Force-Driven Model for Automated Clear Aligner Staging Design Based on Stepwise Tooth Displacement and Rotation in 3D Space

This study introduced a novel force-driven automated staging design method for clear aligners, aimed at enhancing treatment planning efficiency and outcomes. The method simplified the alignment process into a force-driven mechanics model that calculates forces and moments exerted on teeth while adhering to Newton’s third law, determining their displacement and rotation at each position. An optimal path was generated by iteratively moving teeth from their initial to target positions and subsequently divided into stages based on a predefined step size. The algorithm was implemented in C++ and incorporated into the WebGL-based SmarteeCheck3.0 software for visualization. In a maxillary extraction case, the automated staging method (0.25 mm step size) generated 51 stages in merely 5 s, while manual staging (>0.25 mm step size) necessitated 30 min to achieve 55 stages. In a molar distalization case, the automated method demonstrated similar efficiency advantages, generating 30 stages for the maxilla and 34 for the mandible, compared to 41 stages each in manual staging. The automated staging approach yielded shorter and more precise tooth movement paths that adhered to aligner biomechanics and physical principles, surpassing the limitations of manual staging. For cases requiring entire arch displacement, the method incorporated sequential movements with anchorage control to maintain force equilibrium. This innovative method substantially improved design efficiency and accuracy, ultimately elevating the efficacy of clear aligner therapy, although further biomechanical analyses and experimental validations are needed to refine the model parameters.

Ascertaining the Environmental Advantages of Pavement Designs Incorporating Recycled Content through a Parametric and Probabilistic Approach.

Reclaimed asphalt pavement (RAP) is a widely used end-of-life (EoL) material in asphalt pavements to increase the material circularity. However, the performance loss due to using RAP in the asphalt binder layer often requires a thicker layer, leading to additional material usage, energy consumption, and transportation effort. In this study, we developed a parametric and probabilistic life cycle assessment (LCA) framework to robustly compare various pavement designs incorporating recycled materials. Our framework is built upon thermodynamic and physical principles to reveal the complex relationship among the parameters. Mechanistic-Empirical Pavement Design Guide (MEPDG) models and Highway Development Management (HDM4) models are integrated into the framework to estimate pavement roughness and vehicle fuel consumption during the use phase. The pedigree approach and Monte Carlo simulation are integrated into the framework to reflect data uncertainty at the parameter level. We applied the framework to evaluate 66 Flemish motorway segments, revealing that using RAP in the binder layer with increased thickness does not necessarily guarantee lower greenhouse gas (GHG) emissions for pavement construction. However, it may lead to lower GHG emissions due to fuel savings when considering the use phase, highlighting the vital role of the use phase in pavement LCA. Our global sensitivity analysis highlights several contributors (out of 87 parameters) to GHG emissions variance depending on the LCA scope: fuel consumption during the use phase, transport distances, mass of fine aggregate, and machine power and machine productivity during pavement construction. Reducing uncertainties in these parameters can decrease the variance by up to 60%, enhancing discernibility by up to 11%. In conclusion, our parametric and probabilistic LCA framework provides a nuanced understanding when comparing various pavement designs incorporating recycled content, enabling robust decision-making through improved data quality.

Optical Based Methods for Water Monitoring in Biological Tissue.

Skin homeostasis is strongly dependent on its hydration levels, making skin water content measurement vital across various fields, including medicine, cosmetology, and sports science. Noninvasive diagnostic techniques are particularly relevant for clinical applications due to their minimal risk of side effects. A range of optical methods have been developed for this purpose, each with unique physical principles, advantages, and limitations. This review provides an in-depth examination of optical techniques such as diffuse reflectance spectroscopy, optoacoustic spectroscopy, optoacoustic tomography, hyperspectral imaging, and Raman spectroscopy. We explore their efficacy in noninvasive monitoring of skin hydration and edema, which is characterized by an increase in interstitial fluid. By comparing the parameters, sensitivity, and clinical applications of these techniques, this review offers a comprehensive understanding of their potential to enhance diagnostic precision and improve patient care.

Improved Yield Estimation for Heterogeneously Integrated Components

We show the use of a parallelized daisy “field” design for continuity and reliability testing for 2.5D integrated microelectronics. The use of daisy chains for continuity testing of flip-chip integrated microelectronics packaging is well-established, and statistical models are used to estimate the yield and reliability of the interconnects. However, single points of failure make up the basis for these statistical models. This can be a challenge for unknown yield distributions, as longer daisy chain structures provide an accurate probe for high-yielding processes but are of limited use for process development when the yield is lower. More, shorter-length chains are useful in troubleshooting lower-yield situations, at the expense of decreased statistical relevance and increased complexity of data collection due to the necessity of testing a large number of daisy chains. Additionally, information is lost due to a single failure in a daisy chain creating an open circuit that prevents any further failures in a daisy chain from being identified. The new 2D Daisy Field (2DDF) allows for continuous monitoring of emerging failures across a large grid of microbumps for thermocompression flip-chip bonding applications for a wide range of yield/failure rate distributions. The concept is based on failures causing DC resistance changes across a large population of connections, which do not need to be tested individually. The reduced testing complexity allows for continuous monitoring of the entire interconnect population through the resistance across a single pair of connections. Assuming that, at any given time, failures are occurring at single interconnect points (as is frequently the case for fatigue, stress voiding, electromigration-type failures) allows for the determination of the population failure rate in situ. We will present a procedure for eliminating degenerate failure distribution solutions for the resistance from physical principles to determine the unique failure distribution. The technique allows for continuous monitoring of the failure rate, providing yield as a function of an applied parameter: such as temperature, shear, or voltage. Clustering of failures with respect to these parameters provides insight into the onset and mechanisms of failure. Thus, the clear identification of process limits – thermal budgets, mechanical robustness, operating voltages, and so on – for optimizing fabrication can be performed with only a single experiment through the full range of values, rather than a time-consuming guess-and-check process for a number of discrete process parameters until the approximate yield is found. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525. SAND2023-11665A This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA). The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Distribution Statement ““A”” (Approved for Public Release, Distribution Unlimited). If you have any questions, please contact the Public Release Center.

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a metalloprotein binds and the binding structure are still not readily available, even with the predicted protein structure. In this study, we present DisDock, a deep learning method for predicting protein-metal docking. DisDock takes distogram of randomly initialized protein-ligand configuration as input and outputs the distogram of the predicted binding complex. It combines the U-net architecture with self-attention modules to enhance model performance. Taking inspiration from the physical principle that atoms in closer proximity display a stronger mutual attraction, this predictor capitalizes on geometric information to uncover latent characteristics indicative of atom interactions. To train our model, we employ a high-quality metalloprotein dataset sourced from the Mother of All Databases (MOAD). Experimental results demonstrate that our approach outperforms other existing methods in prediction accuracy for various types of metal ions.

Physics-Based Modelling of Plate-Fin Heat Exchangers

Aluminium plate-fin heat exchangers are widely used in automotive, aerospace, and other industrial applications. Extensive research has been conducted on these coolers, yet accurate predictive tools for their thermo-hydraulic performance are still lacking, due to the wide variety of geometric parameters and working fluids involved. This work proposes an original approach based purely on physical principles and established models, combining detailed numerical models for the extended surfaces and manifolds, with global models aimed at accurately evaluating overall head losses and heat transfer rates in plate-fin heat exchangers. Extended surfaces are studied by means of computational models of unitary fin modules under fully developed flow conditions. Entrance effects are analysed through dedicated numerical models. Numerical results on extended surfaces are extended to whole heat exchangers by global models for heat transfer and head losses, based on the ε−NTU method and the Darcy–Weisbach equation, respectively. The proposed approach is presented and validated through the analysis of a case study comprising several heat exchangers featuring different geometries and working fluids. Numerically derived heat transfer rates and head losses are compared with experimental data showing maximum deviations of ±20% for most of the tested configurations, highlighting the strength of the proposed modelling methodology.

Defining quantum games

In this research article, we survey existing quantum physics-related games and, based on this survey, propose a definition for the concept of quantum games. We define a quantum game as any type of rule-based game that either employs the principles of quantum physics or references quantum phenomena or the theory of quantum physics through any of three proposed dimensions: the perceivable dimension of quantum physics, the dimension of quantum technologies, and the dimension of scientific purposes, such as citizen science or education. We also discuss the concept of quantum computer games, which are games on quantum computers, as well as definitions for the concept of science games. Various games explore quantum physics and quantum computing through digital, analogue, and hybrid means, with various incentives driving their development. As interest in games as educational tools for supporting quantum literacy grows, understanding the diverse landscape of quantum games becomes increasingly important. We propose that the three dimensions of quantum games identified in this article be used for designing, analysing, and defining the phenomenon of quantum games.