Physical Modeling Techniques Research Articles

Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-Attention in the Yutian Oasis, Southern Xinjiang, China

Root zone soil moisture (RZSM) plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth. However, the correlation between RZSM and its associated variables, including surface soil moisture (SSM), often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques. Therefore, this study presents a CNN-LSTM-Attention (CLA) model for predicting RZSM. Owing to the scarcity of soil moisture observation data, the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatiotemporal vertical soil moisture. Meteorological data and MODIS vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model. The results of the CLA model for soil moisture prediction within the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models. This was particularly notable at depth of 80–100 cm, where the coefficient of determination (R²) reached nearly 0.9298. Moreover, the RMSE was reduced by 49% and 57% compared with those of the LSTM and CNN-LSTM models, respectively. This study demonstrates that the integration of physical modeling and deep learning techniques provides a more comprehensive and accurate understanding of spatial and temporal soil moisture variations in the root zone.

Attenuation of sound signals in foams for the basic oxygen furnace using physical modelling techniques

Attenuation of sound signals in foams for the basic oxygen furnace using physical modelling techniques

On the Development of a Near-Shore Pivoting Wave Energy Converter

Numerous offshore wave energy converter (WEC) designs have been invented; however, none has achieved full commercialization so far. The primary obstacle impeding WEC commercialization is the elevated levelized cost of energy (LCOE). Consequently, there exists a pressing need to innovate and swiftly diminish the LCOE. A critical challenge faced by WECs is their susceptibility to extreme wave loads during storms. Promising concepts must demonstrate robust design features to ensure resilience in adverse conditions, while maintaining efficiency in harnessing power under normal sea states. It is anticipated that the initial commercial endeavors will concentrate on near-shore WEC technologies due to the cost advantages associated with proximity to the coastline, facilitating more affordable power transmission and maintenance. In response, this manuscript proposes a pioneering near-shore WEC concept designed with a survivability mode that is engineered to mitigate wave loads during severe sea conditions. Moreover, prior investigations have highlighted favorable resonance properties of this novel concept, enhancing wave power extraction during recurrent energetic sea states. This study employs numerical and physical modelling techniques to evaluate wave loads on the proposed WEC. The results indicate a remarkable 65% reduction in wave loads on the moving floater of the WEC during a range of sea states under the implemented survivability mode.

A Systematic Review of Physical Modelling Techniques, Developments and Applications in Slope Stability Analyses

AbstractAn extensive evaluation of published articles suggests a lack of research on a systematic literature review relating to physical modelling techniques, developments and application in slope stability studies. However, there is growing interest in using physical model tests in slope stability investigations. The present study systematically reviews the methodologies and applications of physical modelling in slope stability research. The Scopus database was used to identify relevant studies which employed physical model tests in slope stability investigations. A combination of fifteen keywords was used to identify relevant articles. A PRISMA-P method for conducting a systematic review was adopted. Articles were screened and analysed, and extracted data were re-organised using an Excel sheet. Data relating to research objectives, physical model test techniques, instrumentation, scaling laws, numerical modelling, results, and findings were extracted and analysed. The systematic review highlights gaps requiring further studies, particularly in slope reinforcements using vegetation and strength deterioration of reinforcements performance under repeated loading exposures. It is found that scholars have not fully addressed the influence of loss of water on pore water regimes and its impact on stability when vegetation is applied as reinforcements. It is also found that the development of slope materials for soil slopes in physical model tests relies on the artificial development of such materials with minimal consideration of their long-term behaviour. Although other options, such as bio-cementation and desiccation techniques, which simulate the natural environment of the slopes, are neglected in slope material development, the present study recommends that future studies consider such techniques.

BASES OF THE METHOD OF PHYSICAL MODELING OF THE PROCESS OF CRUSHING OF MUNICIPAL SOLID WASTE

A large number of studies are aimed at increasing the energy efficiency of grinding processes of various solid materials, while maintaining the values ​​of other important indicators, such as material consumption, productivity, etc. Based on this trend, a new method for physically modeling the process of grinding municipal solid waste (MSW) was proposed for the first time. Existing physical modeling techniques are designed for homogeneous and isotropic materials (for example, soil, crushed stone, snow, coal, etc.). The strength properties of solid waste vary widely due to the significant heterogeneity of their components. Consequently, when crushing solid waste, traditional crusher designs have low efficiency in terms of energy intensity, material intensity and product quality. The purpose of this work is to develop a new technique for physical modeling of the grinding process, based on the main principles of similarity theory and modeling, considering the properties of waste heterogeneity. As a result of the research, a block diagram of the physical modeling methodology for the interaction of the working bodies of impact crushing machines with solid waste was developed. A list of tasks for the modeling process and similarity criteria have been determined based on the development of rheological models of the “working body - municipal solid waste” system and the laws of mechanics that characterize the waste grinding process. Based on the developed similarity criteria, scale equations for the grinding process are substantiated and formulas are derived for determining the expected parameters of the original based on the parameters measured on the model. The developed methodology makes it possible to create a crusher design with improved energy efficiency indicators with the least material and labor costs.

Physics-informed differentiable method for piano modeling

Numerical emulations of the piano have been a subject of study since the early days of sound synthesis. High-accuracy sound synthesis of acoustic instruments employs physical modeling techniques which aim to describe the system’s internal mechanism using mathematical formulations. Such physical approaches are system-specific and present significant challenges for tuning the system’s parameters. In addition, acoustic instruments such as the piano present nonlinear mechanisms that present significant computational challenges for solving associated partial differential equations required to generate synthetic sound. In a nonlinear context, the stability and efficiency of the numerical schemes when performing numerical simulations are not trivial, and models generally adopt simplifying assumptions and linearizations. Artificial neural networks can learn a complex system’s behaviors from data, and their application can be beneficial for modeling acoustic instruments. Artificial neural networks typically offer less flexibility regarding the variation of internal parameters for interactive applications, such as real-time sound synthesis. However, their integration with traditional signal processing frameworks can overcome this limitation. This article presents a method for piano sound synthesis informed by the physics of the instrument, combining deep learning with traditional digital signal processing techniques. The proposed model learns to synthesize the quasi-harmonic content of individual piano notes using physics-based formulas whose parameters are automatically estimated from real audio recordings. The model thus emulates the inharmonicity of the piano and the amplitude envelopes of the partials. It is capable of generalizing with good accuracy across different keys and velocities. Challenges persist in the high-frequency part of the spectrum, where the generation of partials is less accurate, especially at high-velocity values. The architecture of the proposed model permits low-latency implementation and has low computational complexity, paving the way for a novel approach to sound synthesis in interactive digital pianos that emulates specific acoustic instruments.

A Survey of Numerical Simulation Tools for Offshore Wind Turbine Systems

The emerging industry of offshore wind turbines mounted on floating bases has garnered significant attention from both academia and industry. The desire to understand the complex physics of these floating structures has led to the development of numerical and physical modelling techniques. While physical testing has traditionally been employed, there is a growing focus on cost-effective and accurate high-fidelity numerical modelling as a potential alternative or supplement. However, commonly used numerical engineering tools in the offshore industry are considered mid- to low-fidelity and may lack the desired precision for floating offshore wind turbines (FOWTs). Given the complexity of these simulation codes, it is crucial to validate their accuracy. To address this, the International Energy Agency (IEA) Wind Technology Collaboration Programme initiated various research endeavors, including the Offshore Code Comparison Collaboration (OC3), Offshore Code Comparison Collaboration Continuation (OC4), Offshore Code Comparison Collaboration Continuation with Correlation (OC5), and the recent Offshore Code Comparison Collaboration Continued with Correlation and Uncertainty (OC6) projects. This study offers a comprehensive survey of the simulation tools available for FOWTs which were part of OC projects, focusing particularly on horizontal axis wind turbines (HAWTs) and highlighting their capabilities and fundamental theories.

Machine Learning and Physics: A Survey of Integrated Models

Predictive modeling of various systems around the world is extremely essential from the physics and engineering perspectives. The recognition of different systems and the capacity to predict their future behavior can lead to numerous significant applications. For the most part, physics is frequently used to model different systems. Using physical modeling can also very well help the resolution of complexity and achieve superior performance with the emerging field of novel artificial intelligence and the challenges associated with it. Physical modeling provides data and knowledge that offer a meaningful and complementary understanding about the system. So, by using enriched data and training phases, the overall general integrated model achieves enhanced accuracy. The effectiveness of hybrid physics-guided or machine learning-guided models has been validated by experimental results of diverse use cases. Increased accuracy, interpretability, and transparency are the results of such hybrid models. In this article, we provide a detailed overview of how machine learning and physics can be integrated into an interactive approach. Regarding this, we propose a classification of possible interactions between physical modeling and machine learning techniques. Our classification includes three types of approaches: (1) physics-guided machine learning (2) machine learning-guided physics, and (3) mutually-guided physics and ML. We studied the models and specifications for each of these three approaches in-depth for this survey.

Grey-box Approach for the Prognostic and Health Management of Lithium-Ion Batteries

The Lithium-Ion Batteries (LIB) industry is rapidly growing and is expected to continue expanding exponentially in the next decade. LIBs are already widely used in everyday life, and their demand is expected to increase further, particularly in the automotive sector. The European Union has introduced a new law to ban Internal Combustion Engines from 2035, pushing for the adoption of electric vehicles and increasing the need for more efficient and reliable energy storage solutions such as LIBs. As a result, the establishment of Gigafactories in Europe and the United States is accelerating to meet the growing demand and partially reduce dependencies on China, which is currently the main producer of LIBs.
 To fully realize the potential of LIBs and ensure their safe and sustainable use, it is crucial to optimize their useful life and develop reliable and robust methodologies for estimating their state of health and predicting their remaining useful life. This requires a comprehensive understanding of LIB behavior and the development of effective prognostic and health management approaches that can accurately predict battery degradation, plan for maintenance and replacements, and improve battery performance and lifespan.
 This work, funded by the GREYDIENT project, a European consortium aiming to advance the state of the art in the grey-box approach, combines physical modeling (white box) and machine learning (black box) techniques to demonstrate the grey-box effectiveness in the Prognostic and Health Management. The grey-box approach here proposed consist in a combination of a physical battery model whose degradation parameters are estimated online at every cycle by a Multi-Layer Perceptron Particle Filter (MLP-PF).
 An electrochemical degradation model of a Lithium-Ion battery cell has been derived by use of Modelica. The model simulates the output voltage of the cell, while the degradation over time is simulate through the variation of 3 parameters: qMax (maximum number of Lithium-Ions available), R0 (Internal Resistance) and D (Diffusion Coefficient). To validate the model we resorted to the well-known NASA Battery Dataset, which has also been used to infer the optimal values of the three hidden degradation parameters at every cycle, to obtain their Run-to-Failure history. Then, the physical model is combined the MLP-PF: a MLPArtificial Neural Network is firstly trained on the Run-to-Failure degradation processes of the model parameters, allowing the propagation of the parameters in the future and the corresponding estimation of the battery Remaining Use ful Life (RUL). The MLP is then updated online by a Particle Filter every time a new measurement is available from the Battery Management System (BMS), providing flexibility to this method, needed for the electrochemical nature of the batteries, and allowing the propagation of uncertainties.

Modelling point mass balance for the glaciers of the Central European Alps using machine learning techniques

Abstract. Glacier mass balance is typically estimated using a range of in situ measurements, remote sensing measurements, and physical and temperature index modelling techniques. With improved data collection and access to large datasets, data-driven techniques have recently gained prominence in modelling natural processes. The most common data-driven techniques used today are linear regression models and, to some extent, non-linear machine learning models such as artificial neural networks. However, the entire host of capabilities of machine learning modelling has not been applied to glacier mass balance modelling. This study used monthly meteorological data from ERA5-Land to drive four machine learning models: random forest (ensemble tree type), gradient-boosted regressor (ensemble tree type), support vector machine (kernel type), and artificial neural networks (neural type). We also use ordinary least squares linear regression as a baseline model against which to compare the performance of the machine learning models. Further, we assess the requirement of data for each of the models and the requirement for hyperparameter tuning. Finally, the importance of each meteorological variable in the mass balance estimation for each of the models is estimated using permutation importance. All machine learning models outperform the linear regression model. The neural network model depicted a low bias, suggesting the possibility of enhanced results in the event of biased input data. However, the ensemble tree-based models, random forest and gradient-boosted regressor, outperformed all other models in terms of the evaluation metrics and interpretability of the meteorological variables. The gradient-boosted regression model depicted the best coefficient of determination value of 0.713 and a root mean squared error of 1.071 m w.e. The feature importance values associated with all machine learning models suggested a high importance of meteorological variables associated with ablation. This is in line with predominantly negative mass balance observations. We conclude that machine learning techniques are promising in estimating glacier mass balance and can incorporate information from more significant meteorological variables as opposed to a simplified set of variables used in temperature index models.

Physical and numerical modeling of the mechanisms of bath entrainment from the aluminum tapping tube intake

The present study aims to investigate the entrainment of the electrolytic bath with aluminum tapping flows from the reduction cell. The physical water modeling technique was used together with numerical simulations.The physical model experiments were carried out in a one-tenth-scale model of an aluminum reduction cell and tapping system. The model comprises a transparent acrylic rectangular container, measuring 800 mm wide, 200 mm, long 300 mm deep and a 26 mm-diameter PVC pipe. Water and canola –oil were used at room temperature to simulate molten aluminum and electrolytic bath, respectively. The modified Froude number-based similarity was used to ensure dynamic similarity between the model and the prototype. Numerical simulations were conducted on a three-dimensional model of an aluminum cell using Ansys Fluent Multiphase and the realizable turbulent k−ε models. Reynolds Averaged Navier -Stokes equations and k−ε realizable turbulence equations were solved using Coupled solver for pressure and velocity coupling by Fluent. The withdrawal pipe positions and leaning angles were considered.From experimental and numerical results, it was observed that the entrainment of oil (simulated bath) increased with the pipe diameter and decreased with the pipe leaning angle. Entrainment was associated with a free surface vortex when the flow was dynamic and wavy with significant circulation. Entrainment occurs without a vortex when the interface between the two fluids drops in a quiescent flow regime.

A novel physical modeling technique for slope failure caused by strength reduction: comparisons with DEM simulation

A novel physical modeling technique for slope failure caused by strength reduction: comparisons with DEM simulation

Using Physical Modeling to Optimize the Aluminium Refining Process.

Concern for the environment and rational management of resources requires the development of recoverable methods of obtaining metallic materials. This also applies to the production of aluminium and its alloys. The quality requirements of the market drive aluminium producers to use effective refining methods, and one of the most commonly used is blowing an inert gas into liquid aluminium via a rotating impeller. The efficiency and cost of this treatment depends largely on the application of the correct ratios between the basic parameters of the process, which are the flow rate of the inert gas, the speed of the rotor and the duration of the process. Determining these ratios in production conditions is expensive and difficult. This article presents the results of research aimed at determining the optimal ratio of the inert gas flow rate to the rotary impeller speed, using physical modeling techniques for the rotor as used in industrial conditions. The tests were carried out for rotary impeller speeds from 150 to 550 rpm and gas flow rates of 12, 17 and 22 dm3/min. The research was carried out on a 1:1 scale physical model, and the results, in the form of visualization of the degree of gas-bubble dispersion, were assessed on the basis of the five typical dispersion patterns. The removal of oxygen from water was carried out analogously to the process of removing hydrogen from aluminium. The curves of the rate of oxygen removal from the model liquid were determined, showing the course of oxygen reduction during refining with the same inert gas flows and rotor speeds mentioned above.

Review on artificial intelligence techniques for improving representative air traffic management capability

The use of artificial intelligence (AI) has increased since the middle of the 20th century, as evidenced by its applications to a wide range of engineering and science problems. Air traffic management (ATM) is becoming increasingly automated and autonomous, making it lucrative for AI applications. This paper presents a systematic review of studies that employ AI techniques for improving ATM capability. A brief account of the history, structure, and advantages of these methods is provided, followed by the description of their applications to several representative ATM tasks, such as air traffic services (ATS), airspace management (AM), air traffic flow management (ATFM), and flight operations (FO). The major contribution of the current review is the professional survey of the AI application to ATM alongside with the description of their specific advantages: (i) these methods provide alternative approaches to conventional physical modeling techniques, (ii) these methods do not require knowing relevant internal system parameters, (iii) these methods are computationally more efficient, and (iv) these methods offer compact solutions to multivariable problems. In addition, this review offers a fresh outlook on future research. One is providing a clear rationale for the model type and structure selection for a given ATM mission. Another is to understand what makes a specific architecture or algorithm effective for a given ATM mission. These are among the most important issues that will continue to attract the attention of the AI research community and ATM work teams in the future.

Evaluation of the possibilities of 3D-printing for the making of waveguide photonic crystals

The paper considers the possibilities of 3D printing technology for the manufacture of X-band waveguide photonic crystals. The photonic crystals for an EIA WR-90 standard rectangular waveguide with a cross section of 22,86 10,16 mm2 in the X-band were designed and manufactured. Numerical modeling of the reflection characteristics of photonic crystals of several designs in the OpenEMS software package was carried out. Based on the simulation results, the best types of the structures were selected, which were manufactured using the physical modeling technique by the fusion method of PETG plastic. Experimental study of the manufactured layouts and comparison of the experimental data with the numerical simulation data were performed. The best types of manufactured models of waveguide photonic crystals showed transmittance and reflection drop in the corresponding frequency bands of the order of 1520 dB, which is acceptable for technical applications. The numerical modeling results are in good agreement with experiments, thus indicating the high efficiency and good control of the topology and dimensions of the manufactured photonic crystals structural elements in the used type of 3D printing technology.

Investigation of Energy Dissipation Rate of Stepped Vertical Overfall (SVO) Spillway Using Physical Modeling and Soft Computing Techniques

In the present study a physical model of a stepped vertical overfall (SVO) spillway is proposed and designed as a novel combination of a free overfall spillway with horizontal steps. First, the hydraulic design characteristics of the proposed spillway were discussed using a laboratory-scaled model. Effective parameters on the energy dissipation rate were defined as the relative critical depth, Froude number, number of steps, and dimensionless steps' geometry parameter using dimensional analysis. The energy dissipation rate of the stepped vertical overfall spillway is measured using a waterwheel laboratory setup. Different geometry and hydraulic scenarios were used to assess the energy dissipation rate variation of the proposed spillway. Furthermore, Support Vector Regression and Random Forest Regression methods were used to estimate the energy dissipation of the proposed structure. Investigating the energy dissipation rate of 27 geometry scenarios with the available range of discharge revealed that the energy dissipation rate against the water's relative depth inside the SVO spillway follows a gradually increasing trend ranging between 88.53% to 98.06%. Also, random forest regression algorithm showed more accurate prediction performance than support vector regression approach with RMSE = 0.128 and R2 = 0.99 in training stage and RMSE = 0.115 and R2 = 0.99 in testing stage. The support vector regression model estimated the proposed spillway's energy dissipation rate with an accuracy of RMSE = 0.67 and R2 = 0.88 in training stage and RMSE = 0.61 and R2 = 0.9 in testing stage.

UCNS3D: An open-source high-order finite-volume unstructured CFD solver

UCNS3D is an open-source computational solver for compressible flows on unstructured meshes. State-of-the-art high-order methods and their associated benefits can now be implemented for industrial-scale CFD problems due to the flexibility and highly-automated generation offered by unstructured meshes. We present the governing equations of the physical models employed in UCNS3D, and the numerical framework developed for their solution. The code has been designed so that extended to other systems of equations and numerical models is straightforward. The employed methods are validated towards a series of stringent well-established test problems against experimental or analytical solutions, where the full capabilities of UCNS3D in terms of applications spectrum, robustness, efficiency, and accuracy are demonstrated. Program summaryProgram title: UCNS3D (Unstructured Compressible Flow Solver)CPC Library link to program files:https://doi.org/10.17632/222zh873kh.1Developer's repository link:https://github.com/ucns3d-team/UCNS3DLicensing provisions: GNU General Public License 3Programming language: Fortran2008Nature of problem: UCNS3D is intended for the simulation of compressible flows in 2D and 3D unstructured meshes, by employing high-resolution, high-order methods capable of providing physically meaningful results in a computational efficient manner. The solver is designed for a broad range of problems encountered in engineering applications such as transitional, fully turbulent, and multicomponent flows with several fidelity level modelling options available.Solution method: The present software includes multiple physical models, numerical methods, and modelling techniques such as iLES, RANS, DES for unstructured meshes. The software has been developed such that the inclusion of additional physical models and numerical methods can be easily accommodated.

Investigation of the effects of managed ice field characteristics on a dynamic positioning AHTS vessel using physical modelling techniques

This paper presents the tools, techniques and outcomes of a model testing program of a dynamically positioned (DP) Ice-1A classed Anchor Handling Tug Supply (AHTS) vessel called the “Magne Viking” in different managed ice environments with ice thickness, concentration, floe size, floe distribution, drift speed, and ice encroachment direction as test variables. A total of 168 test runs were carried out in 7 ice sheets to evaluate the vessel's thruster forces and moment on the horizontal plane and the vessel's 6-DOF motions and accelerations. The time series and probability of exceedance plots of thruster forces and yawing moments are presented, along with the vessel's positioning relative to a set-point in various managed ice conditions. In addition, investigations are carried out to evaluate the influence of various ice field parameters on the time-averaged and time-dependent thruster forces and moment. Both measurements and video recordings confirmed complex and stochastic ice-ship-basin wall interactions and different ice failure modes. Overall, the study demonstrated the capability of the DP-controlled ship to maintain the station for most head-on tests and the oblique ice tests not exceeding 15°. The dataset and video recordings acquired through the testing have improved the understanding of the dynamic ice-vessel interactions.

Ship operation in brash ice: results of investigations

The paper gives a review of the studies concerned with operation of vessels in brash ice. Recently, the ice conditions have received an ever increasing attention of the researchers related to the fact that shipping in the Arctic regions and freezing seas, as well as in inland waterways has been scaled up. One of the important fields of brash ice studies is specifics of sailing under these conditions and primarily determination of the ship ice resistance. The paper shows that theoretical methods combined with physical modeling in ice basins are used for determination of the ship ice resistance under brash ice conditions. The paper traces the evolution of theoretical models utilized for calculations. It is mentioned that the models are mainly based on loose material mechanics. A rapidly developing computer modeling of ship motion in brash ice based on discrete element method is considered. Physical modeling techniques used for modeling brash ice in ice basin are described, and challenges of experimental investigations are discussed. It is pointed out that experimental studies in ice basin can provide valuable data not only about ship ice resistance but also about the mechanisms giving rise to ice channels filled with brash ice. The paper describes the methods for studying operation of ship propellers in brash ice conditions. It is concluded that further research into brash ice is needed.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology.

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.