Robust Parameter Estimation Research Articles

Inferring redshift and galaxy properties via a multi-task neural net with probabilistic outputs. An application to simulated MOONS spectra

The era of large-scale astronomical surveys demands innovative approaches for rapid and accurate analysis of extensive spectral data, and a promising direction in which to address this challenge is offered by machine learning. Here, we introduce a new pipeline M-TOPnet (Multi-Task network Outputting Probabilities), which employs a convolutional neural network with residual learning to simultaneously derive redshift and other key physical properties of galaxies from their spectra. Our tool efficiently encodes spectral information into a latent space, employing distinct downstream branches for each physical quantity, thereby benefiting from multi-task learning. Notably, our method handles the redshift output as a probability distribution, allowing for a more refined and robust estimation of this critical parameter. We demonstrate preliminary results using simulated data from the MOONS instrument, which will soon be operating at the ESO/VLT. We highlight the effectiveness of our tool in accurately predicting the redshift, stellar mass, and star formation rate of galaxies at $z 1-3$, even for faint sources ($m_H 24$) for which traditional methods often struggle. Through analysis of the output probability distributions, we demonstrate that our pipeline enables robust quality screening of the results, achieving accuracy rates of up to 99<!PCT!> in redshift determination (defined as predictions within $| z| < 0.01$ relative to the true redshift) with $8 h$ exposure spectra, while automatically identifying potentially problematic cases. Our pipeline thus emerges as a powerful solution for the upcoming challenges in observational astronomy, combining precision, interpretability, and efficiency, all aspects that are crucial for analysing the massive datasets expected from next-generation instruments.

Analyzing Chemical Decay in Environmental Nanomaterials Using Gamma Distribution with Hybrid Censoring Scheme

This study addresses the challenges of estimating decay times for chemical components, focusing on hydroxylated fullerene C60(OH)29, which poses potential environmental risks due to its persistence and transformation in soil. Given the complexities of real-world experiments such as limited sample availability, time constraints, and the need for efficient resource use, a framework using the Gamma distribution based on hybrid Type-II censoring schemes was developed to model the decay time. The Gamma distribution’s flexibility and mathematical properties make it well-suited for reliability and decay analysis, capturing variable hazard rates and accommodating different censoring structures. We employ maximum likelihood estimation (MLE) and Bayesian methods to estimate the model’s parameters, consequently estimating the reliability and hazard functions. The large sample theory for MLE is used to approximate variances for constructing asymptotic confidence intervals. Additionally, we utilize the Markov chain Monte Carlo technique within the Bayesian framework to ensure robust parameter estimation. Through simulation studies and statistical tests—such as Chi-Square, Kolmogorov–Smirnov, and others—we assess the Gamma distribution’s fit and compare its performance with other distributions, validating the proposed model’s effectiveness.

Training of a physics-based thermo-viscoplasticity model on big data for polypropylene

Research on data-driven constitutive models has demonstrated their outstanding ability to provide highly accurate predictions of the general stress-strain response after learning from data only. Here, we demonstrate that physics-based models can equally benefit from training procedures relying on big data. Specifically, we employ the thermo-mechanically coupled viscoplasticity model [Anand, L., Ames, N.M., Srivastava, V., Chester, S., 2009. A thermo-mechanically coupled theory for large deformations of amorphous polymers. Part 1: Formulation. International Journal of Plasticity] to describe the large deformation response of polypropylene. It combines both mechanism-based evolution equations and high mathematical flexibility. More than 100 constant velocity and strain rate jump experiments are performed on flat tensile specimens extracted from 3 mm thick isotactic polypropylene sheets. The exact cross-sectional area is measured with surround DIC, while an IR camera monitored the surface temperature field. The experiments typically reached true strains greater than 0.8 and cover temperatures and strain rates ranging from 25 to 85 °C and 10–4 to 100s-1, respectively. Training over 100,000 unique random combinations of experiments is performed to identify all model parameters. The effect of the training (and testing) subsets size and composition is carefully analyzed to ensure a high generalization ability. It is found that training based on 26 randomly-selected experiments leads to the most robust parameter estimates. The obtained model performs remarkably well on all our experiments (among which 70 % are unseen during training) with a root mean square error of less than 1.5 MPa. As a byproduct, we also found that there exists a subset of two specific experiments for training that lead to an equally accurate model for polypropylene.

Robust time series clustering of GARCH (1,1) models with outliers

GARCH models can contain outliers, such as Additive Level Outliers (ALO) and/or Additive Volatility Outliers (AVO), so a robust parameter estimation is used to overcome the presence of outliers. This study uses Quasi-Maximum Likelihood Estimation (QMLE) and M-estimation to estimate the parameters of the GARCH model containing outliers. Hierarchical and K-means clustering algorithms are then used to cluster time-series data based on distances calculated from these parameter estimates. The proposed approach applies hierarchical and K-means clustering using distances derived from QMLE and M-estimation results for GARCH (1,1) models containing outliers for clustering with simulation data and a case study using stock data listed in the Indonesia Stock Exchange. The results show that hierarchical and K-means clustering with distance between two GARCH (1,1) models based on M-estimation produces good clusters with smaller C index than QMLE estimations for simulation data and the case study of stock data in Indonesia.

Asymmetric sample shapes complicate planar biaxial testing assumptions by intensifying shear strains and stresses

Planar biaxial testing offers a physiologically relevant approach for mechanically characterizing thin deformable soft tissues, but often relies on erroneous assumptions of uniform strain fields and negligible shear strains and forces. In addition to the complex mechanical behavior exhibited by soft tissues, constraints on sample size, geometry, and aspect ratio often restrict sample shape and symmetry. Using simple PDMS gels, we explored the unknown and unquantified effects of sample shape asymmetry on planar biaxial testing results, including shear strain magnitudes, shear forces measured at the sample’s boundary, and the homogeneity of strains experienced at the center of each sample. We used a combination of finite element modeling and experimental validation to examine PDMS gels of varying levels of asymmetry, allowing us to identify effects of sample shape without confounding factors introduced by the nonlinear, spatially variable, and anisotropic properties of soft tissues. Both biaxial simulations and experiments, which showed strong agreement, revealed that sample shape asymmetry led to significantly larger shear strains, shear forces, and overestimation of principal stresses. Excluding these shear forces resulted in an underestimation of shear moduli during inverse mechanical characterizations. Even in the simplest of deformable biomaterials, sample shape asymmetry should be avoided as it can induce drastic increases in shear strains and shear forces, invalidating traditional planar biaxial testing analyses. Alternatively, sample shape asymmetry may be exploited to generate more robust estimates of constitutive parameters in more complex materials, which could lead to a refined understanding and inference of mechanical behavior.

Advanced parameter estimation for lithium-ion battery model using the information sharing group teaching optimization algorithm

Accurate battery modeling is crucial for optimizing the performance and safety of Lithium-ion batteries (LiBs), particularly in applications such as electric vehicles and smart grids. This paper introduces the Information Sharing Group Teaching Optimization Algorithm (ISGTOA), a novel human-based metaheuristic algorithm designed to estimate the 21 parameters of third-order Equivalent Circuit Model (ECM) for LiBs. Unlike existing methods, ISGTOA effectively manages the increased complexity of a model through advanced group teaching strategies and sophisticated information-sharing mechanisms inspired by classroom dynamics. We validated the effectiveness of ISGTOA using two distinct datasets—High Dynamic Profile (HDP) and Urban Dynamometer Driving Schedule (UDDS)—across various LiB chemistries (NCA and NMC) and temperature conditions (−5 °C, 25 °C and 45 °C). The results demonstrate that ISGTOA achieves superior parameter estimation accuracy and convergence speed compared to established and recent optimization methods such as TLBO, TLSBO, AISA, MTBO, and DTBO. Specifically, ISGTOA attained a minimum RMSE of 8.357 mV with rapid convergence and high stability in the HDP dataset and minimized RMSE to 10 mV within ten iterations at both 25 °C and 45 °C in the UDDS dataset. Additionally, ISGTOA exhibited high efficiency (up to 97.75 %) under varying thermal environments. This work not only introduces a novel algorithm for complex battery modeling but also sets the stage for future advancements in real-time battery management systems, emphasizing the importance of robust, versatile, and efficient parameter estimation techniques.

Results from the further development of CGI inversion on simulation and field data systems

AbstractA new series expansion-based method, the Combined Geoelectric Weighted Inversion (CGWI) procedure is presented and tested by using synthetic and in-field measured datasets. The method is an improved version of the Combined Geoelectric Inversion (CGI) robustified by involving Cauchy-Steiner weights in an Iteratively Reweighted Least Squares technique. The new procedure is compared to the Fourier series expansion-based 1.5D and the CGI methods as well as to the broadly applied RES2DINV inversion procedure. The field measurements are performed during stone exploration in an active quarry on the south-western slopes of the Mátra mountains, in northern Hungary. It is shown that the CGWI method gives stable and robust parameter estimation with acceptable accuracy. The comparison with other inversion methods is based on data distances, estimation errors and correlation parameters calculated on the base of the parameter correlation matrix.

Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods

Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AE-NN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csókás methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.

Driving low-carbon mechanisms through smart investments in renewable resources and green finance initiatives among G20 nations

In light of the escalating concerns regarding climate change and environmental decline, major nations are actively exploring strategies to mitigate environmental harm and achieve future sustainability. The surge in economic expansion in developed economies is linked to an increase in CO2 emissions. Consequently, in their pursuit of carbon-neutral policies, these countries are increasingly turning towards renewable energy as a means to enhance resource conservation and efficiency. This research investigates the varied impacts of renewable energy investment, green finance, geopolitical risk, GDP growth, foreign direct investment, and gross fixed capital formation on the carbon emissions of G20 countries. The study uses the CUP-FM (Continuously Updated Fully Modified) and CUP-BC (Continuously Updated Bias-Corrected) estimators, which are sophisticated econometric approaches designed to handle non-stationary panel data and cross-sectional dependency, to produce robust long-term parameter estimates. The CUP-FM estimator adjusts for potential endogeneity and serial correlation, improving the accuracy of long-run relationships in panel data. The CUP-BC estimator provides bias-corrected estimates to further enhance the precision of these long-run connections.The long-term parameter estimates reveal a negative correlation between renewable energy investment, green finance, and carbon emissions. In contrast, foreign direct investment, gross fixed capital formation, GDP growth, and geopolitical risk are positively associated with CO2 emissions. This suggests that financial stability often leads to investments in carbon-heavy economic ventures, thereby implicating economic growth as a contributing factor to environmental degradation in G20 countries.

Rapid sloshing-free transport of liquids in arbitrarily shaped containers

We present a model-based feedforward control strategy suitable for designing swift rest-to-rest maneuvers for liquids in arbitrarily shaped containers. We employ the commonly used equivalent pendulum model to represent the sloshing dynamics and suggest a novel parameter identification scheme suitable for arbitrary container shapes and any number of sloshing modes. By computing natural modes and fluid reaction forces and torques for imposed harmonic container motions via a finite element model, we obtain data for the identification scheme. A fitting procedure then yields highly accurate parameters for a physical pendulum model, where each pendulum represents one sloshing mode. We also provide a thorough analysis of parameter identifiability and guidelines for obtaining robust parameter estimates. The proposed feedforward control method uses a virtual tray pendulum on which we place the container (in the form of its equivalent pendulum model). Designing the virtual tray such that the fluid’s dominant sloshing mode cannot be excited by horizontally moving the tray pendulum pivot effectively zeros out any sloshing motion in this mode. We then exploit the flatness property of the resulting system to design rest-to-rest maneuvers where any residual sloshing motion (in higher modes) can be exactly stopped at the end of the maneuver. The effectiveness of the proposed method is demonstrated through extensive simulations and experimental results using a Martini cocktail glass, whose shape is challenging in terms of sloshing. The experimental results show the successful, accurate suppression of sloshing, validating the efficacy of the proposed concept.

Optimizing parameter learning and calibration in an integrated hydrological model: Impact of observation length and information

Integrated hydrological modeling is gaining popularity due to its mechanistic representation of the surface and subsurface processes. However, estimating the parameters of such process-based models can be computationally expensive if careful consideration is not given to the length of streamflow observations used during model calibration. Here we evaluate the influence of the calibration period, the role of streamflow information content, and the gage location in parameter learning and calibration of a fully integrated hydrological model, the Advanced Terrestrial Simulator (ATS). We conducted the study at the Upper Neversink River Watershed within the Delaware River Basin, where streamflow observations are available at 11 gages with varying record lengths. We leveraged a recently proposed knowledge-informed deep learning technique for parameter estimation. To assess the impact of observation period and gage location, model parameters were learned on scenarios using different chunks of streamflow observations, including (1) using only one year or consecutive multiple years of streamflow observations at the watershed outlet and (2) using one shared year of observations at each of the sub-catchment gages, with the period from 1991-10-01 through 1999-09-30 as the overall calibration period. Using the estimated parameters, ATS was rerun for each scenario and evaluated on a subsequent period from 1999-10-01 through 2002-09-30. Results show that the basin outlet discharge prediction is mostly improved when using at least four years of observations for parameter estimation. Further, the performance of the calibrated ATS run correlates with the information content of the observed streamflows, suggesting that the information-theoretic metrics could be indicators for selecting the observation period for parameter estimation. Finally, we find that observations from an informative gage can be used in learning parameters to predict the streamflow at a nearby gage, which would potentially lower the computational expense by reducing the watershed domain used in calibration. Our success underscores the potential of using information theory to achieve robust model parameter estimation on a reduced computational budget.

Online numerical simulation method based on adaptive unscented Kalman filter for shear wall structures

Hybrid test method is an effective approach to evaluate the seismic performance of engineering structures. Particularly, online numerical simulation method (ONSM) based on unscented Kalman filter (UKF) model updating significantly improves the accuracy of the hybrid test results. However, the sensitivity of UKF to the initial values of the state vector, state covariance, and observation noise covariance may lead to the distortion of hybrid test results. To address these issues, one novel ONSM based on adaptive UKF (ONSM-AUKF) is proposed in this paper. The AUKF algorithm incorporates an adaptive variance module to maintain equilibrium. This not only reduces the sensitivity of UKF to the initial parameter settings, prevents filter divergence, but also enhances the accuracy and stability of estimation. The proposed ONSM-AUKF accurately identifies the constitutive model parameters by utilizing measured data from specimens through the AUKF algorithm, which improves the accuracy and the stability of parameter estimation. The simulation results indicate that AUKF algorithm has the capability to decrease the sensitivity of initial parameter settings, thus enhancing the stability and robustness of parameter estimation in comparison to the UKF algorithm. The experimental results validate the feasibility and effectiveness of the proposed ONSM-AUKF, as well as the advantages of the ONSM-AUKF over the ONSM-UKF. The results indicate that the proposed ONSM-AUKF may have broad application prospect in evaluating the seismic performance of various of complex engineering structures.

Integrating Bayesian inference and neural ODEs for microgrids dynamics parameters estimation

The integration of solar and wind energy sources in microgrids has witnessed significant growth, giving rise to distinct challenges due to their intermittent nature when it comes to achieving efficient microgrid control. However, estimating the parameters of the dynamic microgrid components facilitates capturing the complex and time-varying characteristics of renewable energy generation. This requires an accurate estimation of the parameters from the dynamic differential equations for effective modeling and control. In this research paper, we presented a novel methodology based on the integration of Bayesian inference and Neural ODEs. The Bayesian inference quantifies the uncertainty, and the Neural ODEs model the dynamic systems. By combining the strengths of both methods, we aimed to achieve a precise and robust parameter estimation of the dynamic microgrid components. The methodology is validated on a simulated microgrid that consists of a diesel generator, Solar PV array, double-fed induction generator, and a battery energy storage system. The results showed promised inferences estimation obtained from the parameter posterior distribution even in the presence of uncertainty. This can enhance our understanding of the dynamics of renewable energy systems and can contribute to the advancement of decision-making microgrid control strategies.

Parameter estimation from the Lyα forest in the Fourier space using an information-maximizing neural network

Our aim is to present a robust parameter estimation with simulated forest spectra from Sherwood-Relics simulations suite by using an information-maximizing neural network (IMNN) to extract maximal information from 1D-transmitted flux in the Fourier space. We performed 1D estimations using IMNN for intergalactic medium (IGM) thermal parameters $T_0$ and gamma at $z=2-4$, and cosmological parameters $ and s $ at $z=3-4$. We compared our results with estimates from the power spectrum using the posterior distribution from a Markov Chain Monte Carlo (MCMC). We then checked the robustness of IMNN estimates against deviation in spectral noise levels, continuum uncertainties, and instrumental smoothing effects. Using mock forest sightlines from the publicly available CAMELS project, we also checked the robustness of the trained IMNN on a different simulation. As a proof of concept, we demonstrated a 2D-parameter estimation for $T_0$ and photoionization rates, $ HI We obtain improved estimates of $T_0$ and gamma using IMNN over the standard MCMC approach. These estimates are also more robust against signal-to-noise deviations at $z=2$ and 3. At $z=4$, the sensitivity to noise deviations is on par with MCMC estimates. The IMNN also provides $T_0$ and gamma estimates that are robust against continuum uncertainties by extracting small-scale continuum-independent information from the Fourier domain. In the cases of $ and s $, the IMNN performs on par with MCMC but still offers a significant speed boost in estimating parameters from a new dataset. The improved estimates with IMNN are seen for high instrumental resolution (FWHM=6 At medium or low resolutions, the IMNN performs similarly to MCMC, suggesting an improved extraction of small-scale information with IMNN. We also find that IMNN estimates are robust against the choice of simulation. By performing a 2D-parameter estimation for $T_0$ and $ HI $, we also demonstrate how to take forward this approach observationally in the future.

Robust Function-on-Function Regression: A Penalized Tau-based Estimation Approach

This study introduces a novel penalized estimation method tailored for function-on-function regression models, combining the robustness of the Tau estimator with penalization techniques to enhance resistance to outliers. Function-on-function regression is essential for modeling intricate relationships between functional predictors and response variables across diverse fields. However, traditional methods often struggle with outliers, leading to biased estimates and diminished predictive performance. Our proposed approach addresses this challenge by integrating robust Tau estimation with penalization, promoting both robustness and parsimony in parameter estimation. Theoretical foundations of the penalized Tau estimator within function-on-function regression are discussed, along with empirical validations through simulation studies and an empirical data analysis. By incorporating penalization, our method not only ensures robust estimation of regression parameters but also promotes model simplicity, offering enhanced interpretability and generalization capabilities in functional data analysis.

A Robust Approach using M-Estimation for Dynamic Panel Autoregressive Model

This paper presents a robust M-estimation approach for first-order panel autoregressive models, addressing the challenges posed by high persistence levels of the autoregressive parameter and individual heterogeneity. Generalized method of moments estimators widely used in dynamic panel models exhibit substantial finite sample biases and are sensitive to weak instruments, particularly as the autoregressive parameter gets close to unity. Our proposed weighted M-estimator, which uses a power function for the scale parameter in Huber’s loss function, offers a robust alternative. By minimizing the variance of model parameters through an optimal tuning parameter, our method enhances the efficiency and robustness of parameter estimates. We demonstrate the superiority of the proposed approach through several Monte-Carlo simulations and an application to hydro-electric power output data, providing comprehensive comparisons with existing generalized method of moments estimators.

Locally sparse and robust partial least squares in scalar-on-function regression

We present a novel approach for estimating a scalar-on-function regression model, leveraging a functional partial least squares methodology. Our proposed method involves computing the functional partial least squares components through sparse partial robust M regression, facilitating robust and locally sparse estimations of the regression coefficient function. This strategy delivers a robust decomposition for the functional predictor and regression coefficient functions. After the decomposition, model parameters are estimated using a weighted loss function, incorporating robustness through iterative reweighting of the partial least squares components. The robust decomposition feature of our proposed method enables the robust estimation of model parameters in the scalar-on-function regression model, ensuring reliable predictions in the presence of outliers and leverage points. Moreover, it accurately identifies zero and nonzero sub-regions where the slope function is estimated, even in the presence of outliers and leverage points. We assess our proposed method’s estimation and predictive performance through a series of Monte Carlo experiments and an empirical dataset—that is, data collected in relation to oriented strand board. Compared to existing methods our proposed method performs favorably. Notably, our robust procedure exhibits superior performance in the presence of outliers while maintaining competitiveness in their absence. Our method has been implemented in the robsfplsr package in .

A hierarchical Bayesian approach for parameter identification in structural deterioration models using probabilistic surrogate models

Most structural systems currently in operation are subjected to potentially multiple deterioration processes, for which different models exist to describe their evolution in time. The quantities of interest (QoIs) involved in them are often directly unobservable. However, advances in Structural Health Monitoring (SHM) have enabled gathering structural response data which can be used to indirectly make inferences on unobservable (state) quantities through Bayesian inference. Obtaining updated estimates of deterioration model parameters, given observed data, enables them to be used to predict future health states of interest for the structure, which constitutes a core tenet of condition-based maintenance (CBM). Key to obtaining robust parameter estimates is accounting for different uncertain quantities involved in the model that transforms unobservable quantities to observable ones, which in a Bayesian context is achieved through the likelihood function. This work presents an offline, flexible hierarchical framework to tackle this problem that employs probabilistic reduced order models (ROMs) in the construction of the likelihood function, that enable marginalizing out these uncertain quantities. The approximate likelihood function is then used within a typical Markov Chain Monte Carlo (MCMC) setting for parameter estimation. The framework is demonstrated in applications related to deterioration processes common to ship structures.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures.

Digital twinning of cardiac electrophysiology for congenital heart disease.

In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.