Ana Larrañaga

Many engineering problems involve phenomena whose governing equations are poorly characterized or only partially known. Surrogate modeling techniques such as neural networks can capture the behavior of these systems, but they typically demand large training datasets that are difficult to obtain in engineering contexts and yield models with limited physical interpretability. The Sparse Identification of Nonlinear Dynamics (SINDy) method addresses both limitations by performing sparse regression over libraries of candidate nonlinear terms, recovering interpretable governing equations from comparatively small datasets. Although SINDy has been demonstrated extensively on canonical benchmark systems, its application to practical engineering problems is less widely documented. This tutorial introduces the SINDy method and progressively builds toward its main extensions, from noise-robust weak-form and ensembling-based variants to constrained and parametrizable formulations. The paper and the accompanying tutorial (available online) is organized in three parts: the first introduces the standard SINDy algorithm and progressively extends it, inviting readers without prior knowledge to follow each step and adapt the methods to their own problems; the remaining two parts present detailed case studies on (1) the system identification of an unmanned aerial vehicle and (2) a chaotic thermosyphon heat exchanger. Through these examples, we aim to demonstrate that SINDy is simple to implement yet flexible enough to serve as a valuable identification tool for advanced engineering applications.

Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensing devices, or even within a single experiment. This work addresses the problem of discovering nonlinear dynamical systems from such inhomogeneous data. We extend the Sparse Identification of Nonlinear Dynamical Systems (SINDy) framework to account for variable noise levels by combining Ensemble SINDy and Weak SINDy within a weighted regression formulation derived from generalized least squares. A statistical justification for the weighting strategy is also provided. The methodology is validated on several benchmark systems, including ordinary and partial differential equations. In addition, we show the benefit of multi-fidelity integration for forecasting the dynamics of a double pendulum system. The results confirm that the proposed approach mitigates the adverse effects of heteroscedastic noise and that repeated, low-cost, low-quality measurements can improve model recovery, in some cases matching or outperforming reconstructions obtained using only high-fidelity data.

The fidelity of a quantum gate is sensitive to small deviations in the physical control parameters. Unfortunately, it is generally difficult to exactly model the implemented Hamiltonian for a set of user-defined parameters, necessitating on-device calibration. Here, we present an active learning framework based on Bayesian optimization with a Gaussian Process surrogate to find the optimal parameter set. We validate the technique through numerical calibration of the laser amplitude and frequencies that implement the trapped-ion Mølmer Sørensen gate. We show that a Gaussian process can model the Hamiltonian dynamics. The addition of active learning accelerates the discovery of the optimal parameter set with speed and final fidelity dependent on the quantum projection noise of the data. These results establish the utility of active learning and surrogate models for quantum calibration and control.

The Fuel Optimizer is an inverse design framework for sustainable aviation fuels that starts from a user-defined merit function and identifies the optimal combination of chemical species or hydrocarbon families that optimize a combination of targets. As a case study, a database of fuel blends meeting selected property standards was simulated in a reactor model to obtain pollutant emissions at cruise conditions. A surrogate model was developed to reduce the computational cost of evaluating candidate blends, taking fuel composition as input and predicting emissions as output. A genetic algorithm was used to optimize fuel formulation according to two merit functions designed to break the nitrogen oxides - CO trade-off, and minimize pollutant emissions over the landing-and-take-off cycle. Constraints included selected property standards (including seal swelling) and composition limits. Fuel candidates from the framework outperformed the training database across all merit functions, and the optimal candidates were validated through reactor simulations.

Identifying the governing equations of complex dynamical systems remains a fundamental challenge across science and engineering. While early approaches relied on empirical data and heuristics, modern data-driven methods offer greater flexibility and fewer assumptions. However, data acquisition in real-world settings is often expensive. This work addresses this challenge by introducing an active learning strategy for dynamics discovery in the ultra-low data limit. Rather than sampling randomly, our method iteratively prioritizes regions that are most informative for model identification. This approach builds on Sparse Identification of Nonlinear Dynamics (SINDy), and utilizes an ensemble extension, E-SINDy, to estimate epistemic uncertainty and guide the sampling for both ordinary and partial differential equations (ODEs/PDEs). For ODEs, an exhaustive analysis is conducted on the Lorenz system across varying data budgets and noise levels. For PDEs, two systems with contrasting dynamical characteristics are examined: the Burgers’ equation, where a sharp shock front creates a distinction between informative and uninformative regions, and the Kuramoto-Sivashinsky equation, which presents a more spatially complex sampling landscape. Across all scenarios, the proposed method accurately identifies the governing dynamics with significantly fewer data samples than random sampling.

Recent research on engine combustion has focused on new less carbon-intensive alternatives that meet current standards and can serve as drop-in fuels. However, developing surrogate fuels is challenging due to the difficulty of predicting key properties such as derived cetane number, which quantifies the ignition quality in compression ignition engines. Higher values indicate highly reactive fuels whereas lower values indicates longer ignition times that will lead to misfiring and increased emissions in diesel engines. The measurement of this property entails the test of the candidate fuels under specific conditions, making it costly and time-consuming. This work explores the use of data-driven models to predict the derived cetane number of both single-component fuels and blends. NREL’s compendium is used as reference, while the ignition quality tester is replaced by a 1D spray model coupled with detailed chemical kinetics. The models are trained on fuel composition, basic thermophysical properties, and simulated ignition delay. Additionally, symbolic regression is employed to derive an explicit equation linking derived cetane number to key fuel descriptors. The resulting expression achieves comparable accuracy to state-of-the-art neural networks (MAE 3.5, R^{2} 0.96–0.97), but reveals a physically interpretable structure where ignition delay dominates predictions, with double bond equivalent and heat of vaporization acting as corrective terms for specific chemical families, enhancing generalizability across diverse fuels. The accuracy of the resulting models is then compared to the available literature. This research provides a robust framework for predicting derived cetane number in unconventional fuels, enabling the exploration of new candidates for on-road, off-road, and aviation applications.

Microfin design for industrial applications has been extensively studied in the past, but the use of innovative techniques that can reveal hitherto unfamiliar morphologies realizable with novel additive manufacturing techniques has not yet been fully exploited. Here, an attempt is made to develop a structured methodology for the assessment of various randomly generated geometries, with the purpose of identifying the most significant geometrical parameters for their thermohydraulic performance. To do so, a computational fluid dynamics (CFD) database consisting of nearly 16,000 geometrical configurations was used, and various feature extraction algorithms were analyzed and compared from a classical, mechanistic perspective, within a structured framework called data interconnectivity and correlation for explainability, or DICE for short. The input parametric search space of geometries is composed of 15 features that represent the coordinates of the vertices of the microfin and the periodic separation between them. Based on data-driven models trained with heat flux and total drag as separate targets, a reduction to just three crucial geometrical parameters is achieved. The physical reasons behind this reduced set of key parameters are discussed. In addition, based on gene expression programming, two mathematical expressions were produced that, with only six of the inputs, are able to model the performance of the parameterized microfin array with a relative error of 13.82% for the heat flux and 3.40% for the total drag. Ultimately, this study successfully addresses the challenge of managing complex, high-dimensional feature spaces through a comprehensive analysis of explainability.

Background: The development of new large-scale saliva pooling detection strategies can significantly enhance testing capacity and frequency for asymptomatic individuals, which is crucial for containing SARS-CoV-2.

Objective: This study aims to implement and scale-up a SARS-CoV-2 screening method using pooled saliva samples to control the virus in critical areas and assess its effectiveness in detecting asymptomatic infections.

Methods: Between August 2020 and February 2022, our laboratory received a total of 928,357 samples. Participants collected at least 1 mL of saliva using a self-sampling kit and registered their samples via a smartphone app. All samples were directly processed using AutoMate 2550 for preanalytical steps and then transferred to Microlab STAR, managed with the HAMILTON Pooling software for pooling. The standard pool preset size was 20 samples but was adjusted to 5 when the prevalence exceeded 2% in any group. Real-time polymerase chain reaction (RT-PCR) was conducted using the Allplex SARS-CoV-2 Assay until July 2021, followed by the Allplex SARS-CoV-2 FluA/FluB/RSV assay for the remainder of the study period.

Results: Of the 928,357 samples received, 887,926 (95.64%) were fully processed into 56,126 pools. Of these pools, 4863 tested positive, detecting 5720 asymptomatic infections. This allowed for a comprehensive analysis of pooling’s impact on RT-PCR sensitivity and false-negative rate (FNR), including data on positive samples per pool (PPP). We defined Ctref as the minimum cycle threshold (Ct) of each data set from a sample or pool and compared these Ctref results from pooled samples with those of the individual tests (ΔCtP). We then examined their deviation from the expected offset due to dilution [ΔΔCtP = ΔCtP – log2]. In this work, the ΔCtP and ΔΔCtP were 2.23 versus 3.33 and –0.89 versus 0.23, respectively, comparing global results with results for pools with 1 positive sample per pool. Therefore, depending on the number of genes used in the test and the size of the pool, we can evaluate the FNR and effective sensitivity (1 – FNR) of the test configuration. In our scenario, with a maximum of 20 samples per pool and 3 target genes, statistical observations indicated an effective sensitivity exceeding 99%. From an economic perspective, the focus is on pooling efficiency, measured by the effective number of persons that can be tested with 1 test, referred to as persons per test (PPT). In this study, the global PPT was 8.66, reflecting savings of over 20 million euros (US $22 million) based on our reagent prices.

Conclusions: Our results demonstrate that, as expected, pooling reduces the sensitivity of RT-PCR. However, with the appropriate pool size and the use of multiple target genes, effective sensitivity can remain above 99%. Saliva pooling may be a valuable tool for screening and surveillance in asymptomatic individuals and can aid in controlling SARS-CoV-2 transmission. Further studies are needed to assess the effectiveness of these strategies for SARS-CoV-2 and their application to other microorganisms or biomarkers detected by PCR.

Currently, there is a growing industry-wide focus on enhancing the thermohydraulic performances of devices, with the goal of achieving more efficient energy management. However, due to manufacturing constraints, there has been a tendency to focus on simple geometric designs. Recently, the emergence of additive manufacturing techniques and advancements in artificial intelligence have enabled new possibilities in this field. In this work, a pioneering exploration of a novel methodology for optimization of microfins to enhance the heat transfer on flat surfaces is presented. The optimization is performed using data-driven algorithms to accelerate the evaluation of the performance of these surfaces; these algorithms are trained on a database of 15,694 numerical simulations of enhanced surfaces. The performance evaluation criterion (PEC), equal to the ratio between the thermal and hydraulic performance parameters of the geometry, is used as the objective function. To avoid an optimization that focuses solely on the compactness of the fins while ignoring their shape, the optimal geometry is sought, which proves to be a challenge. Hence, an optimization method that classifies the surfaces based on their periodicity is proposed, focusing on improving the performance in terms of the morphology. Results present a PEC augmentation range from +0.08 to +0.28.

This paper presents a theoretical model that calculates the thermal parameters of a triple tube heat exchanger (TTHE) analogously to the formulation used for double tube heat exchangers. The model calculates the outlet temperatures, heat exchange and thermal efficiency of the TTHE from the inlet conditions and flow properties in a simple and efficient formulation that allows for the easy calculation of multiple cases to analyze the TTHE behavior under different conditions. The model also proposes a theoretical optimum distribution of the flows to optimize heat transfer. The theoretical model is compared with detailed CFD simulations and experimental data of straight 1-meter-long and U-shaped 6-meter-long TTHE systems. The theoretical model shows average errors of 7% and 12%, and the CFD simulation shows 5% and 10% errors, both for straight and U-shaped TTHEs, respectively. The model error is reasonably low since the overall heat transfer coefficients used in the model are based on heat transfer theoretical correlations, the accuracy of which is usually low. In addition, a parametric study on the distribution of the inner tube and the outer annulus mass flow rate ratio was performed to determine the optimum distribution of a particular case.