Intersection Of Machine Learning Research Articles

Deep Learning in Computational Design Synthesis: A Comprehensive Review

Abstract A paradigm shift in the computational design synthesis (CDS) domain is being witnessed by the onset of the innovative usage of machine learning techniques. The rapidly evolving paradigmatic shift calls for systematic and comprehensive assimilation of extant knowledge at the intersection of machine learning and computational design synthesis. Understanding nuances, identifying research gaps, and outlining the future direction for cutting-edge research is imperative. This article outlines a hybrid literature review consisting of a thematic and framework synthesis survey to enable conceptual synthesis of information at the convergence of computational design, machine learning, and big data models. The thematic literature survey aims at conducting an in-depth descriptive survey along the lines of a broader theme of machine learning in computational design. The framework synthesis-based survey tries to encapsulate the research findings in a conceptual framework to understand the domain better. The framework is based on the CDS process, which consists of four submodules: representation, generation, evaluation, and guidance. Each submodule has undergone an analysis to identify potential research gaps and formulate research questions. In addition, we consider the limitations of our study and pinpoint the realms where the research can be extended in the future.

Exploring the impact of machine learning on dance performance: a systematic review

ABSTRACT In recent years, the intersection of machine learning and dance has garnered increasing attention as a means to enhance the aesthetics and creativity of dance performance. Machine learning techniques, such as human-pose detection, have been utilized to analyze body movement and generate visual models in realtime, offering new approaches to traditional art form. However, despite the growing interest in this field, there is a lack of comprehensive research that evaluates the impact of machine learning on dance from multiple perspectives. This systematic review addresses this gap by examining the impact of machine learning on four key aspects of dance performance: choreographic creation support; dataset or network collection and training; improved techniques of human-pose detection; perception and new visual representations of dance movement. This exploration reveals that machine learning contributes both positively and negatively to artistic development, introducing a sense of novelty and heightened interest in the performance while concurrently prompting ethical concerns regarding artistic ownership and authorship. Through a nuanced analysis of machine learning's effects on key aspects of dance, this study aims to provide valuable insights into how these technologies can enhance the artistic practice in dance while signaling areas that demand further research in this rapidly evolving field.

Bibliometrics Analysis on Using Machine Learning Algorithms in Teacher Education Researches

Machine learning (ML) techniques hold promise for innovating teacher preparation and development programs. However, the current state of research leveraging artificial intelligence in teacher-focused contexts remains unclear. This study undertook a systematic bibliometric analysis to characterize the emerging domain investigating ML applications for enhancing teacher effectiveness. Using the bibliographic R tool Bibliometrix, metadata of 740 English-language articles published during 2019-2023 extracted from Web of Science educational databases were examined to determine performance metrics, science mapping, citation networks, and research trends situating at the intersection of ML and teacher education. Document growth averaged 39.57% annually, with collaborations involving 87% of publications and 21.62% engaging international co-authorships. The USA led productivity metrics, though opportunities exist to expand geographical diversity. Analyses revealed research activity presently concentrates around employing ML for student analytics, assessment frameworks, and online learning environments. Highly cited works dealt with ML systems for evaluation and competency modeling of teachers rather than directly supporting pedagogical practice. Significant gaps persist exploring intelligent recommendation engines and affective computing chatbots tailored to teachers’ dynamic training needs and emotional responses. This bibliometric review synthesizes the contours and trends in investigating ML applications for augmenting teachers’ capabilities. Findings inform stakeholders to mobilize efforts strategically advancing this domain for enriching classrooms.

Summary of the 1st Interpretability and Robustness in Neural Software Engineering (InteNSE 2023)

InteNSE is an interdisciplinary workshop for research at the intersection of Machine Learning (ML) and Software Engineering (SE) and would be a pioneer in emphasizing the implicit properties of neural software engineering and analysis. Due to recent computational advancements, ML has become an inseparable part of the SE research community. ML can indeed improve and revolutionize many SE tasks. However, most research in the AI and SE communities consider ML as a closed box, i.e., only considering the final performance of the developed models as an evaluation metric. Ignoring the implicit properties of neural models, such as interpretability and robustness, one cannot validate the model's actual performance, generalizability, and whether it is learning what it is supposed to do. Specifically, in the domain of SE, where the result of ML4SE tools is code synthesis, bug finding, or repair, interpretability and robustness are crucial to ensure the reliability of the products.

Exploring Huntington's Disease Diagnosis via Artificial Intelligence Models: A Comprehensive Review.

Huntington's Disease (HD) is a devastating neurodegenerative disorder characterized by progressive motor dysfunction, cognitive impairment, and psychiatric symptoms. The early and accurate diagnosis of HD is crucial for effective intervention and patient care. This comprehensive review provides a comprehensive overview of the utilization of Artificial Intelligence (AI) powered algorithms in the diagnosis of HD. This review systematically analyses the existing literature to identify key trends, methodologies, and challenges in this emerging field. It also highlights the potential of ML and DL approaches in automating HD diagnosis through the analysis of clinical, genetic, and neuroimaging data. This review also discusses the limitations and ethical considerations associated with these models and suggests future research directions aimed at improving the early detection and management of Huntington's disease. It also serves as a valuable resource for researchers, clinicians, and healthcare professionals interested in the intersection of machine learning and neurodegenerative disease diagnosis.

Seizure Detection Via Time Series Classification Using Modified Metaheuristic Optimized Recurrent Networks

Epilepsy, colloquially termed seizure disorder, constitutes a neurological condi-tion characterized by unpredictable and sudden episodes of heightened electrical activity in the brain. The inherent risks associated with seizures, particularly in their nascent stages, stem from their challenging predictability, often leading to individuals endangering themselves through falls or lapses into unconsciousness in precarious settings. Additionally, the potential for neurological damage further compounds the complexity of managing this condition. This research endeavors to explore the viability of Recurrent Neural Networks (RNNs) for the early detection of seizures using electroencephalogram (EEG) recordings derived from real-world scenarios. Acknowledging the pivotal role of network topology and training parameters in achieving optimal performance, this study employs meta-heuristic algorithms to fine-tune performance through judicious hyperparameter selection. Notably, a modified iteration of a recently introduced algorithm is introduced herein. The application of RNNs to seizure detection yields promising outcomes, with constructed models achieving an accuracy surpassing 99%. The results obtained underscore the potential of employing metaheuristic-optimized RNNs in seizure detection as a means of substantially enhancing the quality of life for individuals afflicted by epilepsy. The demonstrated accuracy of these models suggests a robust and reliable methodology, paving the way for future advancements in the realm of neurotechnological interventions. This investigation contributes valuable insights into the intersection of machine learning, neuroscience, and healthcare, offering a foundation for further research and technological developments aimed at ameliorating the challenges posed by epilepsy.

Integrating Machine Learning Techniques for Advancing Industry 4.0: Opportunities, Challenges, and Future Directions

This paper explores the integration of machine learning techniques in the context of Industry 4.0, aiming to enhance automation, optimize processes, and enable data-driven decision-making in industrial settings. It provides an overview of Industry 4.0, highlighting its evolution and transformative impact on manufacturing. The paper also examines various machine learning algorithms and their adaptability to diverse industrial applications. Challenges including data quality, security, and scalability are addressed, along with case studies illustrating successful machine learning implementations. Future trends and research directions at the intersection of machine learning and Industry 4.0 are outlined, providing valuable insights for researchers and practitioners. This research offers a foundational framework for navigating the dynamic landscape of machine learning within Industry 4.0.

Application and Challenges of Machine Learning in Healthcare

Abstract: The integration of machine learning (ML) techniques into healthcare has emerged as a transformative force, revolutionizing various aspects of patient care, disease management, and healthcare operations. This research paper explores the manifold applications and accompanying challenges associated with the utilization of ML in healthcare. Machine learning finds extensive application in healthcare, encompassing early disease detection, personalized treatment plans, drug discovery, medical image analysis, and patient risk stratification. It plays a pivotal role in clinical decision support, enhancing diagnostic accuracy and treatment effectiveness. Furthermore, ML-based telemedicine and remote monitoring solutions have expanded healthcare accessibility, particularly in remote or underserved areas. Despite its remarkable potential, the adoption of ML in healthcare is not without challenges. Data privacy and security concerns are paramount, as sensitive patient information is processed. Data quality, interoperability issues, and ethical considerations related to algorithm bias and transparency demand vigilant attention. Regulatory hurdles and resistance to change among healthcare professionals add complexity to the integration process. Ethical considerations emerge prominently as healthcare providers increasingly rely on ML-driven insights. This paper discusses the ethical dimensions surrounding patient data privacy, informed consent, and the need for transparent and unbiased algorithms. Looking ahead, the research identifies future trends and opportunities in the intersection of ML and healthcare. As technology evolves, AI ethics and responsible AI principles will play a pivotal role in shaping the ethical framework of healthcare. Real-world case studies underscore the significant impact of ML in healthcare and provide valuable insights into success factors and challenges faced in various healthcare contexts. In conclusion, machine learning holds great promise in revolutionizing healthcare, but its implementation necessitates addressing complex challenges, especially ethical concerns. This paper serves as a comprehensive overview of the state of ML in healthcare, offering recommendations for stakeholders and a vision for an ethically-driven, technology-empowered future in healthcare

A Survey of Data Quality Requirements That Matter in ML Development Pipelines

The fitness of the systems in which Machine Learning (ML) is used depends greatly on good-quality data. Specifications on what makes a good-quality dataset have traditionally been defined by the needs of the data users—typically analysts and engineers. Our article critically examines the extent to which established data quality frameworks are applicable to contemporary use cases in ML. Using a review of recent literature at the intersection of ML, data management, and human-computer interaction, we find that the classical “fitness-for-use” view of data quality can benefit from a more stage-specific approach that is sensitive to where in the ML lifecycle the data are encountered. This helps practitioners to plan their data quality tasks in a manner that meets the needs of the stakeholders who will encounter the dataset, whether it be data subjects, software developers or organisations. We therefore propose a new treatment of traditional data quality criteria by structuring them according to two dimensions: (1) the stage of the ML lifecycle where the use case occurs vs. (2) the main categories of data quality that can be pursued (intrinsic, contextual, representational and accessibility). To illustrate how this works in practice, we contribute a temporal mapping of the various data quality requirements that are important at different stages of the ML data pipeline. We also share some implications for data practitioners and organisations that wish to enhance their data management routines in preparation for ML.

Evaluation of the energy viability of smart IoT sensors using TinyML for computer vision applications: A case study

TinyML technology emerges from the intersection of Machine Learning, Embedded Systems, and Internet of Things (IoT), and presents itself as a solution for various IoT fields. For this technology to be successfully applied to embedded devices, it is essential that these devices have adequate energy efficiency. To demonstrate the viability of TinyML technology on embedded devices, field re- search and real experiments were conducted. An embedded system was installed in a turnstile of a Federal Institute, in which a TinyML computer vision model for people detection was implemented. The device counts the number of people, analyzes the battery level, and sends data in real-time to the cloud. The prototype showed promising results, and studies were conducted with a lithium battery and three in series. In these experiments, voltage consumption was analyzed every hour, and the results were presented through graphs. The camera sensor prototype had a consumption of 1.25 volts/hour, while the prototype without the camera sensor showed a longer-lasting consumption of 0.93 volts/hour. This field research will contribute to the advancement of applications and studies related to TinyML in conjunction with IoT and computer vision.

Is There a Trojan! : Literature Survey and Critical Evaluation of the Latest ML Based Modern Intrusion Detection Systems in IoT Environments

IoT as a domain has grown so much in the last few years that it rivals that of the mobile network environments in terms of data volumes as well as cybersecurity threats. The confidentiality and privacy of data within IoT environments have become very important areas of security research within the last few years. More and more security experts are interested in designing robust IDS systems to protect IoT environments as a supplement to the more traditional security methods. Given that IoT devices are resource-constrained and have a heterogeneous protocol stack, most traditional intrusion detection approaches don’t work well within these schematic boundaries. This has led security researchers to innovate at the intersection of Machine Learning and IDS to solve the shortcomings of non-learning based IDS systems in the IoT ecosystem. Despite various ML algorithms already having high accuracy with IoT datasets, we can see a lack of sufficient production grade models. This survey paper details a comprehensive summary of the latest learning-based approaches used in IoT intrusion detection systems, and conducts a through critical review of these systems, potential pitfalls in ML pipelines, challenges from an ML perspective and discusses future research scope, and recommendations.

A bibliometric analysis of worldwide cancer research using machine learning methods.

With the progress and development of computer technology, applying machine learning methods to cancer research has become an important research field. To analyze the most recent research status and trends, main research topics, topic evolutions, research collaborations, and potential directions of this research field, this study conducts a bibliometric analysis on 6206 research articles worldwide collected from PubMed between 2011 and 2021 concerning cancer research using machine learning methods. Python is used as a tool for bibliometric analysis, Gephi is used for social network analysis, and the Latent Dirichlet Allocation model is used for topic modeling. The trend analysis of articles not only reflects the innovative research at the intersection of machine learning and cancer but also demonstrates its vigorous development and increasing impacts. In terms of journals, Nature Communications is the most influential journal and Scientific Reports is the most prolific one. The United States and Harvard University have contributed the most to cancer research using machine learning methods. As for the research topic, "Support Vector Machine," "classification," and "deep learning" have been the core focuses of the research field. Findings are helpful for scholars and related practitioners to better understand the development status and trends of cancer research using machine learning methods, as well as to have a deeper understanding of research hotspots.

Intraday trend prediction of stock indices with machine learning approaches

In recent years, as research at the intersection of machine learning and finance has grown, predicting stock price movements has become a particularly intriguing issue. Current research focuses primarily on using historical data of the previous day to predict stock movements for the following day, whereas fewer studies use the trading day’s opening data to predict market movements for the current day. We predict intraday price movements of the SSE-50 (Shanghai Securities 50 Index) using stock market opening data as input. Specifically, decision tree, extreme gradient boosting (XGBoost), random forest, support vector machines (SVM), and long-short-term memory are developed to predict the movements of the SSE-50 index utilizing opening price data of various time intervals. We also design three trading strategies when different time frequencies of data are used. At the same time-frequency, the results demonstrate that SVM with Gaussian and linear kernels outperform others. The forecasting accuracy at 10-min frequency approaches 70%, which is close to the results at longer time intervals, indicating that intraday trend can be determined by opening price fluctuations and the first 10-min data contains sufficient information to predict the trend for the entire trading day. In addition, trading methods based on the forecast of daily, weekly, and monthly SSE-50 price movement outperform buy-and-hold strategies. Daily trading performs better than the other two strategies. The outcomes of this research can expand the use of machine learning in quantitative trading and enrich intraday trading techniques further.

AI’s 10 to Watch, 2022

AI’s 10 to Watch, 2022

AI-driven innovation in ethnic clothing design: an intersection of machine learning and cultural heritage

<abstract><p>This study delves into the innovative application of Artificial Intelligence (AI) and machine learning algorithms in the realm of ethnic fashion design, with a specific emphasis on the Miao women's apparel. We introduce an AI-powered approach that strategically bridges modern technology with traditional elements, denoting a significant stride in the field of fashion design. Our research underscores three major aspects: customization of body shape, fabric selection, and innovative design. An AI-driven statistical methodology was utilized to accurately adapt to the unique body characteristics of Miao women, demonstrating an application of machine learning in pattern recognition. Furthermore, the AI's capacity to analyze the fabric properties was harnessed to optimize material selection, creating a balance between aesthetics and comfort. The innovative use of the Multimodal Unsupervised Image-to-Image Translation (MUNIT) algorithm, an AI tool, generated diverse and trendy designs, thereby enriching the distinctiveness of ethnic apparel. Our study accentuates the synergistic blend of traditional crafting methods and modern technological applications, highlighting the role of AI in the sustainable development of ethnic fashion. Additionally, we also demonstrate the advantages of Made-to-Measure (MTM) approaches, emphasizing the importance of individual customization in contemporary fashion design. This research presents a pioneering exploration at the nexus of AI, pattern recognition, and ethnic fashion design, which has the potential to transform the future of the fashion industry.</p></abstract>

The Use of Machine Learning to Reduce Overtreatment of the Axilla in Breast Cancer: Retrospective Cohort Study.

Patients with early breast cancer undergoing primary surgery, who have low axillary nodal burden, can safely forego axillary node clearance (ANC). However, routine use of axillary ultrasound (AUS) leads to 43% of patients in this group having ANC unnecessarily, following a positive AUS. The intersection of machine learning with medicine can provide innovative ways to understand specific risks within large patient data sets, but this has not yet been trialed in the arena of axillary node management in breast cancer. The objective of this study was to assess if machine learning techniques could be used to improve preoperative identification of patients with low and high axillary metastatic burden. A single-center retrospective analysis was performed on patients with breast cancer who had a preoperative AUS, and the specificity and sensitivity of AUS were calculated. Standard statistical methods and machine learning methods, including artificial neural network, naive Bayes, support vector machine, and random forest, were applied to the data to see if they could improve the accuracy of preoperative AUS to better discern high and low axillary burden. The study included 459 patients; 142 (31%) had a positive AUS; among this group, 88 (62%) had 2 or fewer macrometastatic nodes at ANC. Logistic regression outperformed AUS (specificity 0.950 vs 0.809). Of all the methods, the artificial neural network had the highest accuracy (0.919). Interestingly, AUS had the highest sensitivity of all methods (0.777), underlining its utility in this setting. We demonstrated that machine learning improves identification of the important subgroup of patients with no palpable axillary disease, positive ultrasound, and more than 2 metastatically involved nodes. A negative ultrasound in patients with no palpable lymphadenopathy is highly indicative of low axillary burden, and it is unclear whether sentinel node biopsy adds value in this situation. Further studies with larger patient numbers focusing on specific breast cancer subgroups are required to refine these techniques in this setting.

Equation‐Free Surrogate Modeling of Geophysical Flows at the Intersection of Machine Learning and Data Assimilation

AbstractThere is a growing interest in developing data‐driven reduced‐order models for atmospheric and oceanic flows that are trained on data obtained either from high‐resolution simulations or satellite observations. The data‐driven models are non‐intrusive in nature and offer significant computational savings compared to large‐scale numerical models. These low‐dimensional models can be utilized to reduce the computational burden of generating forecasts and estimating model uncertainty without losing the key information needed for data assimilation (DA) to produce accurate state estimates. This paper aims at exploring an equation‐free surrogate modeling approach at the intersection of machine learning and DA in Earth system modeling. With this objective, we introduce an end‐to‐end non‐intrusive reduced‐order modeling (NIROM) framework equipped with contributions in modal decomposition, time series prediction, optimal sensor placement, and sequential DA. Specifically, we use proper orthogonal decomposition (POD) to identify the dominant structures of the flow, and a long short‐term memory network to model the dynamics of the POD modes. The NIROM is integrated within the deterministic ensemble Kalman filter (DEnKF) to incorporate sparse and noisy observations at optimal sensor locations obtained through QR pivoting. The feasibility and the benefit of the proposed framework are demonstrated for the NOAA Optimum Interpolation Sea Surface Temperature (SST) V2 data set. Our results indicate that the NIROM is stable for long‐term forecasting and can model dynamics of SST with a reasonable level of accuracy. Furthermore, the prediction accuracy of the NIROM gets improved by almost one order of magnitude by the DEnKF algorithm.

Manipulating vulnerability: Poisoning attacks and countermeasures in federated cloud–edge–client​ learning for image classification

The cloud–edge–client hierarchical computing architecture takes into account the dual advantages of cloud and edge computing. However, in realistic scenarios, the explosive increase of distributed data promotes the widespread application of the cloud–edge–client architecture, but also brings a variety of security threats. Relying on the advantages of federated learning technology, this has led to a growing interest in federated learning as the intersection of machine learning and cloud–edge–client fields. However, recent studies found that federated learning is potentially vulnerable to data poisoning attacks. Existing explorations of federated learning poisoning attacks and defense techniques in the cloud–edge–client environment are largely attack capabilities-specific and privacy protection-based: they are designed but fail to consider the characteristics of federated learning vulnerability and the difference in attack intensity, and lack flexibility. Yet few different attack capabilities and general defense strategies have been developed. To address these concerns, we introduce federated cloud–edge–client learning collaborative framework. Based on this architecture, a client-launched multi-label flipping attack method is discussed, which exhibits different attack capabilities. Further, we propose SPAD, a Server-based Parameter Adaptive Defense method in response to different attack intensities, to identify malicious attackers and benign participants, thereby improving the security of the federated learning image classification task. The results on image classification tasks demonstrate that in cloud–edge–client scenario, the multi-label flipping attack method presents different degrees of poisoning effect, and SPAD method has strong feasibility and effectiveness against poisoning attacks, which verify the significant advantages of the proposed method.

Challenges and opportunities in quantum machine learning.

At the intersection of machine learning and quantum computing, quantum machine learning has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry and high-energy physics. Nevertheless, challenges remain regarding the trainability of quantum machine learning models. Here we review current methods and applications for quantum machine learning. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with quantum machine learning.

JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows

Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes equations describe fluid flows and are representative of nonlinear physical systems with complex spatio-temporal interactions. Fluid flows are omnipresent in nature and engineering applications, and their accurate simulation is essential for providing insights into these processes. While PDEs are typically solved with numerical methods, the recent success of machine learning (ML) has shown that ML methods can provide novel avenues of finding solutions to PDEs. ML is becoming more and more present in computational fluid dynamics (CFD). However, up to this date, there does not exist a general-purpose ML-CFD package which provides 1) powerful state-of-the-art numerical methods, 2) seamless hybridization of ML with CFD, and 3) automatic differentiation (AD) capabilities. AD in particular is essential to ML-CFD research as it provides gradient information and enables optimization of preexisting and novel CFD models. In this work, we propose JAX-Fluids: a comprehensive fully-differentiable CFD Python solver for compressible two-phase flows. JAX-Fluids is intended for ML-supported CFD research. The framework allows the simulation of complex fluid dynamics with phenomena like three-dimensional turbulence, compressibility effects, and two-phase flows. Written entirely in JAX, it is straightforward to include existing ML models into the proposed framework. Furthermore, JAX-Fluids enables end-to-end optimization. I.e., ML models can be optimized with gradients that are backpropagated through the entire CFD algorithm, and therefore contain not only information of the underlying PDE but also of the applied numerical methods. We believe that a Python package like JAX-Fluids is crucial to facilitate research at the intersection of ML and CFD and may pave the way for an era of differentiable fluid dynamics. Program summaryProgram title: JAX-FluidsCPC Library link to program files:https://doi.org/10.17632/pzvkwn5s6p.1Developer's repository link:https://github.com/tumaer/JAXFLUIDSCode Ocean capsule:https://codeocean.com/capsule/6819679Licensing provisions: GNU GPLv3Programming language: PythonSupplementary material: Source code; Examples; Videos: Moving solid bodies, Taylor-Green vortex, Rising bubble, Shock-bubble interaction.Nature of problem: The compressible Navier-Stokes equations describe continuum-scale fluid flows. These flows often involve highly complex flow phenomena such as shocks, material interfaces, and turbulence. The intrinsic nonlinear dynamics render the numerical simulation of the these equations challenging. Machine learning provides novel avenues for describing partial differential equations. Machine learning models rely on gradient information provided by automatic differentiation and are often implemented in Python. In contrast, existing high-performance computational fluid dynamics codes are typically written in Fortran or C++ and do not offer inherent automatic differentiation capabilities. These discrepancies hinder the advance of machine-learning-supported computational fluid dynamics. Up to this day, a general-purpose fully-differentiable computational fluid dynamics solver for compressible two-phase flows is missing.Solution method: We introduce JAX-Fluids: a general-purpose three-dimensional fully-differentiable computational fluid dynamics solver for compressible two-phase flows. JAX-Fluids is a simulation framework intended for machine-learning-supported computational fluid dynamics research. Our framework is written entirely in JAX, a high-performance numerical computing library with automatic differentiation capabilities. We have used an object-oriented programming style and a modular design philosophy. This allows the straightforward exchange of numerical methods. We provide a wide variety of state-of-the-art high-order computational methods for compressible flows. The modularity of our framework additionally facilitates the integration of custom subroutines. We use the sharp-interface level-set method to model two-phase flows. The software package can easily be installed as a Python package. We have build the source code around the JAX NumPy API. This makes JAX-Fluids accessible and performant. JAX-Fluids runs on CPUs, GPUs, and TPUs. We use HDF5 in combination with XDMF for writing output quantities. The Python packages Haiku and Optax are used for implementation and training of machine learning methods.Additional comments including restrictions and unusual features: JAX-Fluids relies on open-source third-party Python libraries. These are automatically installed. In the current version, JAX-Fluids only runs on a single accelerator (CPU/GPU/TPU). Future versions will include support for parallel execution. JAX-Fluids has been tested on Linux and macOS operating systems.