https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Fri, 10 Jul 2026 11:42:32 GMT 2026-07-10T11:42:32Z http://hdl.handle.net/10985/24661 Identification of material parameters in low-data limit: application to gradient-enhanced continua NGUYEN, Duc-Vinh; JEBAHI, Mohamed; CHAMPANEY, Victor; CHINESTA SORIA, Francisco Due to the growing trend towards miniaturization, small-scale manufacturing processes have become widely used in various engineering fields to manufacture miniaturized products. These processes generally exhibit complex size effects, making the behavior of materials highly dependent on their geometric dimensions. As a result, accurate understanding and modeling of such effects are crucial for optimizing manufacturing outcomes and achieving high-performance final products. To this end, advanced gradient-enhanced plasticity theories have emerged as powerful tools for capturing these complex phenomena, offering a level of accuracy significantly greater than that provided by classical plasticity approaches. However, these advanced theories often require the identification of a large number of material parameters, which poses a significant challenge due to limited experimental data at small scales and high computation costs. The present paper aims at evaluating and comparing the effectiveness of various optimization techniques, including evolutionary algorithm, response surface methodology and Bayesian optimization, in identifying the material parameter of a recent flexible gradient-enhanced plasticity model developed by the authors. The paper findings represent an attempt to bridge the gap between advanced material behavior theories and their practical industrial applications, by offering insights into efficient and reliable material parameter identification procedures. Mon, 01 Jan 2024 00:00:00 GMT http://hdl.handle.net/10985/24661 2024-01-01T00:00:00Z NGUYEN, Duc-Vinh JEBAHI, Mohamed CHAMPANEY, Victor CHINESTA SORIA, Francisco Due to the growing trend towards miniaturization, small-scale manufacturing processes have become widely used in various engineering fields to manufacture miniaturized products. These processes generally exhibit complex size effects, making the behavior of materials highly dependent on their geometric dimensions. As a result, accurate understanding and modeling of such effects are crucial for optimizing manufacturing outcomes and achieving high-performance final products. To this end, advanced gradient-enhanced plasticity theories have emerged as powerful tools for capturing these complex phenomena, offering a level of accuracy significantly greater than that provided by classical plasticity approaches. However, these advanced theories often require the identification of a large number of material parameters, which poses a significant challenge due to limited experimental data at small scales and high computation costs. The present paper aims at evaluating and comparing the effectiveness of various optimization techniques, including evolutionary algorithm, response surface methodology and Bayesian optimization, in identifying the material parameter of a recent flexible gradient-enhanced plasticity model developed by the authors. The paper findings represent an attempt to bridge the gap between advanced material behavior theories and their practical industrial applications, by offering insights into efficient and reliable material parameter identification procedures. http://hdl.handle.net/10985/25773 Optimal velocity planning based on the solution of the Euler-Lagrange equations with a neural network based velocity regression GHNATIOS, Chady; DI LORENZO, Daniele; CHAMPANEY, Victor; CUETO, Elias; CHINESTA SORIA, Francisco Trajectory optimization is a complex process that includes an infinite number of possibilities and combinations. This work focuses on a particular aspect of the trajectory optimization, related to the optimization of a velocity along a predefined path, with the aim of minimizing power consumption. To tackle the problem, a functional formulation and minimization strategy is developed, by means of the Euler-Lagrange equation. The minimization is later performed using a neural network approach. The strategy is deemed Lagrange-Net, as it is based on the minimization of the energy functional, by the means of Lagrange's equation and neural network approximations. Mon, 01 Jul 2024 00:00:00 GMT http://hdl.handle.net/10985/25773 2024-07-01T00:00:00Z GHNATIOS, Chady DI LORENZO, Daniele CHAMPANEY, Victor CUETO, Elias CHINESTA SORIA, Francisco Trajectory optimization is a complex process that includes an infinite number of possibilities and combinations. This work focuses on a particular aspect of the trajectory optimization, related to the optimization of a velocity along a predefined path, with the aim of minimizing power consumption. To tackle the problem, a functional formulation and minimization strategy is developed, by means of the Euler-Lagrange equation. The minimization is later performed using a neural network approach. The strategy is deemed Lagrange-Net, as it is based on the minimization of the energy functional, by the means of Lagrange's equation and neural network approximations. http://hdl.handle.net/10985/22196 Parametric analysis and machine learning-based parametric modeling of wire laser metal deposition induced porosity LOREAU, Tanguy; CHAMPANEY, Victor; HASCOET, Nicolas; LAMBARRI, Jon; MADARIETA, Mikel; GARMENDIA, Iker; CHINESTA SORIA, Francisco Additive manufacturing is an appealing solution to produce geometrically complex parts, difficult to manufacture using traditional technologies. The extreme process conditions, in particular the high temperature, complex interactions and couplings, rich metallurgical transformations and combinatorial deposition trajectories, induce numerous process defects and in particular porosity. Simulating numerically porosity appearance remains extremely complex because of the multiple physics induced by the laser-material interaction, the multiple space and time scales, with a strong impact on the simulation efficiency and performances. Moreover, when analyzing parts build-up by using the wire laser metal deposition —wLMD— technology it can be noticed a significant variability in the porosity size and distribution even when process parameters remain unchanged. For these reasons the present paper aims at proposing an alternative modeling approach based on the use of neural networks to express the porosity as a function of different process parameters that will be extracted from the process analysis. Fri, 01 Apr 2022 00:00:00 GMT http://hdl.handle.net/10985/22196 2022-04-01T00:00:00Z LOREAU, Tanguy CHAMPANEY, Victor HASCOET, Nicolas LAMBARRI, Jon MADARIETA, Mikel GARMENDIA, Iker CHINESTA SORIA, Francisco Additive manufacturing is an appealing solution to produce geometrically complex parts, difficult to manufacture using traditional technologies. The extreme process conditions, in particular the high temperature, complex interactions and couplings, rich metallurgical transformations and combinatorial deposition trajectories, induce numerous process defects and in particular porosity. Simulating numerically porosity appearance remains extremely complex because of the multiple physics induced by the laser-material interaction, the multiple space and time scales, with a strong impact on the simulation efficiency and performances. Moreover, when analyzing parts build-up by using the wire laser metal deposition —wLMD— technology it can be noticed a significant variability in the porosity size and distribution even when process parameters remain unchanged. For these reasons the present paper aims at proposing an alternative modeling approach based on the use of neural networks to express the porosity as a function of different process parameters that will be extracted from the process analysis. http://hdl.handle.net/10985/22637 Multiparametric modelling of composite materials based on non-intrusive PGD informed by multiscale analyses: Application for real-time stiffness prediction of woven composites EL FALLAKI IDRISSI, Mohammed; PRAUD, Francis; CHAMPANEY, Victor; CHINESTA SORIA, Francisco; MERAGHNI, Fodil In this paper, a multiparametric solution of the stiffness properties of woven composites involving several microstructure parameters is performed. For this purpose, non-intrusive PGD-based methods are employed. From offline pre-computed solutions generated through a full-field multiscale modeling, the proposed method approximates the multidimensional solution as a sum of products of one-dimensional functions each depending on a single variable. The present work aims at providing an accurate approximation of this multiparametric solution with lower computational cost for dataset generation. Thus, a comparative analysis of three non-intrusive PGD formulations (SSL, s-PGD and ANOVA-PGD) is carried out. The obtained results reveal and demonstrate that the ANOVA-PGD model works well for approximating the stiffness properties over the entire parameter space, i.e., along its boundary as well as inside it, by using only few pre-computed high-fidelity solutions. Finally, a GUI application is developed to exploit this multiparametric solution by incorporating other composite weave architectures. This application could be easily used by engineers and composite designers, to deduce, in real-time, the macroscopic properties of woven composite for a given set of microstructural parameters by simply varying the cursors and without any microstructure generation and meshing nor FE computations using periodic homogenization. Thu, 01 Sep 2022 00:00:00 GMT http://hdl.handle.net/10985/22637 2022-09-01T00:00:00Z EL FALLAKI IDRISSI, Mohammed PRAUD, Francis CHAMPANEY, Victor CHINESTA SORIA, Francisco MERAGHNI, Fodil In this paper, a multiparametric solution of the stiffness properties of woven composites involving several microstructure parameters is performed. For this purpose, non-intrusive PGD-based methods are employed. From offline pre-computed solutions generated through a full-field multiscale modeling, the proposed method approximates the multidimensional solution as a sum of products of one-dimensional functions each depending on a single variable. The present work aims at providing an accurate approximation of this multiparametric solution with lower computational cost for dataset generation. Thus, a comparative analysis of three non-intrusive PGD formulations (SSL, s-PGD and ANOVA-PGD) is carried out. The obtained results reveal and demonstrate that the ANOVA-PGD model works well for approximating the stiffness properties over the entire parameter space, i.e., along its boundary as well as inside it, by using only few pre-computed high-fidelity solutions. Finally, a GUI application is developed to exploit this multiparametric solution by incorporating other composite weave architectures. This application could be easily used by engineers and composite designers, to deduce, in real-time, the macroscopic properties of woven composite for a given set of microstructural parameters by simply varying the cursors and without any microstructure generation and meshing nor FE computations using periodic homogenization. http://hdl.handle.net/10985/22377 Parametric Curves Metamodelling Based on Data Clustering, Data Alignment, POD-Based Modes Extraction and PGD-Based Nonlinear Regressions CHAMPANEY, Victor; PASQUALE, Angelo; AMMAR, Amine; CHINESTA SORIA, Francisco In the context of parametric surrogates, several nontrivial issues arise when a whole curve shall be predicted from given input features. For instance, different sampling or ending points lead to non-aligned curves. This also happens when the curves exhibit a common pattern characterized by critical points at shifted locations (e.g., in mechanics, the elasticplastic transition or the rupture point for a material). In such cases, classical interpolation methods fail in giving physics-consistent results and appropriate pre-processing steps are required. Moreover, when bifurcations occur into the parametric space, to enhance the accuracy of the surrogate, a coupling with clustering and classification algorithms is needed. In this work we present several methodologies to overcome these issues. We also exploit such surrogates to quantify and propagate uncertainty, furnishing parametric stastistical bounds for the predicted curves. The procedures are exemplified over two problems in Computational Mechanics. Wed, 01 Jun 2022 00:00:00 GMT http://hdl.handle.net/10985/22377 2022-06-01T00:00:00Z CHAMPANEY, Victor PASQUALE, Angelo AMMAR, Amine CHINESTA SORIA, Francisco In the context of parametric surrogates, several nontrivial issues arise when a whole curve shall be predicted from given input features. For instance, different sampling or ending points lead to non-aligned curves. This also happens when the curves exhibit a common pattern characterized by critical points at shifted locations (e.g., in mechanics, the elasticplastic transition or the rupture point for a material). In such cases, classical interpolation methods fail in giving physics-consistent results and appropriate pre-processing steps are required. Moreover, when bifurcations occur into the parametric space, to enhance the accuracy of the surrogate, a coupling with clustering and classification algorithms is needed. In this work we present several methodologies to overcome these issues. We also exploit such surrogates to quantify and propagate uncertainty, furnishing parametric stastistical bounds for the predicted curves. The procedures are exemplified over two problems in Computational Mechanics. http://hdl.handle.net/10985/20595 Data-Driven Modeling for Multiphysics Parametrized Problems-Application to Induction Hardening Process DEROUICHE, Khouloud; GAROIS, Sevan; CHAMPANEY, Victor; DAOUD, Monzer; TRAIDI, Khalil; CHINESTA SORIA, Francisco Data-driven modeling provides an efficient approach to compute approximate solutions for complex multiphysics parametrized problems such as induction hardening (IH) process. Basically, some physical quantities of interest (QoI) related to the IH process will be evaluated under real-time constraint, without any explicit knowledge of the physical behavior of the system. Hence, computationally expensive finite element models will be replaced by a parametric solution, called metamodel. Two data-driven models for temporal evolution of temperature and austenite phase transformation, during induction heating, were first developed by using the proper orthogonal decomposition based reduced-order model followed by a nonlinear regression method for temperature field and a classification combined with regression for austenite evolution. Then, data-driven and hybrid models were created to predict hardness, after quenching. It is shown that the results of artificial intelligence models are promising and provide good approximations in the low-data limit case. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10985/20595 2021-01-01T00:00:00Z DEROUICHE, Khouloud GAROIS, Sevan CHAMPANEY, Victor DAOUD, Monzer TRAIDI, Khalil CHINESTA SORIA, Francisco Data-driven modeling provides an efficient approach to compute approximate solutions for complex multiphysics parametrized problems such as induction hardening (IH) process. Basically, some physical quantities of interest (QoI) related to the IH process will be evaluated under real-time constraint, without any explicit knowledge of the physical behavior of the system. Hence, computationally expensive finite element models will be replaced by a parametric solution, called metamodel. Two data-driven models for temporal evolution of temperature and austenite phase transformation, during induction heating, were first developed by using the proper orthogonal decomposition based reduced-order model followed by a nonlinear regression method for temperature field and a classification combined with regression for austenite evolution. Then, data-driven and hybrid models were created to predict hardness, after quenching. It is shown that the results of artificial intelligence models are promising and provide good approximations in the low-data limit case. http://hdl.handle.net/10985/22207 Engineering empowered by physics-based and data-driven hybrid models: A methodological overview CHAMPANEY, Victor; CHINESTA SORIA, Francisco; CUETO, Elias Smart manufacturing implies creating virtual replicas of the processing operations, taking into account the material dimension and its multi-physics transformation when forming processes operate. Performing efficient, that is, online accurate predictions of the induced properties (including potential defects) of the formed part (to optimally control the process parameters) needs moving beyond usual offline simulation based on nominal models, and proceeds by assimilating data. This will serve, from one side, to keep the model calibrated, and from the other, to enrich the model and its associated predictions, to avoid bias, to improve accuracy or for performing online diagnosis, by advertising on preventive maintenance. For all these purposes, a new alliance between physics-based and data-driven modelling approaches seems a very valuable route for empowering engineering in general, and smart manufacturing in particular. The present paper revisits the main methodologies involved in the construction of the component or system Hybrid Twins. Tue, 05 Apr 2022 00:00:00 GMT http://hdl.handle.net/10985/22207 2022-04-05T00:00:00Z CHAMPANEY, Victor CHINESTA SORIA, Francisco CUETO, Elias Smart manufacturing implies creating virtual replicas of the processing operations, taking into account the material dimension and its multi-physics transformation when forming processes operate. Performing efficient, that is, online accurate predictions of the induced properties (including potential defects) of the formed part (to optimally control the process parameters) needs moving beyond usual offline simulation based on nominal models, and proceeds by assimilating data. This will serve, from one side, to keep the model calibrated, and from the other, to enrich the model and its associated predictions, to avoid bias, to improve accuracy or for performing online diagnosis, by advertising on preventive maintenance. For all these purposes, a new alliance between physics-based and data-driven modelling approaches seems a very valuable route for empowering engineering in general, and smart manufacturing in particular. The present paper revisits the main methodologies involved in the construction of the component or system Hybrid Twins. http://hdl.handle.net/10985/19416 Parametric Electromagnetic Analysis of Radar-Based Advanced Driver Assistant Systems VERMIGLIO, Simona; CHAMPANEY, Victor; SANCARLOS, Abel; DAIM, Fatima; KEDZIA, Jean Claude; DUVAL, Jean Louis; DIEZ, Pedro; CHINESTA SORIA, Francisco Efficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave propagation. Because of the very high frequency at which these devices operate, with the associated extremely small wavelength, very fine meshes are needed to accurately discretize the electromagnetic equations. Thus, the computational cost of each numerical solution for a given choice of the design or operation parameters, is high (CPU time consuming and needing significant computational resources) compromising the efficiency of standard optimization algorithms. In order to alleviate the just referred difficulties the present paper proposes an approach based on the use of reduced order modeling, in particular the construction of a parametric solution by employing a non-intrusive formulation of the Proper Generalized Decomposition, combined with a powerful phase-angle unwrapping strategy for accurately addressing the electric and magnetic fields interpolation, contributing to improve the design, the calibration and the operational use of those systems. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/19416 2020-01-01T00:00:00Z VERMIGLIO, Simona CHAMPANEY, Victor SANCARLOS, Abel DAIM, Fatima KEDZIA, Jean Claude DUVAL, Jean Louis DIEZ, Pedro CHINESTA SORIA, Francisco Efficient and optimal design of radar-based Advanced Driver Assistant Systems (ADAS) needs the evaluation of many different electromagnetic solutions for evaluating the impact of the radome on the electromagnetic wave propagation. Because of the very high frequency at which these devices operate, with the associated extremely small wavelength, very fine meshes are needed to accurately discretize the electromagnetic equations. Thus, the computational cost of each numerical solution for a given choice of the design or operation parameters, is high (CPU time consuming and needing significant computational resources) compromising the efficiency of standard optimization algorithms. In order to alleviate the just referred difficulties the present paper proposes an approach based on the use of reduced order modeling, in particular the construction of a parametric solution by employing a non-intrusive formulation of the Proper Generalized Decomposition, combined with a powerful phase-angle unwrapping strategy for accurately addressing the electric and magnetic fields interpolation, contributing to improve the design, the calibration and the operational use of those systems. http://hdl.handle.net/10985/22204 Surrogate parametric metamodel based on Optimal Transport TORREGROSA, Sergio; CHAMPANEY, Victor; AMMAR, Amine; HERBERT, Vincent; CHINESTA SORIA, Francisco The description of a physical problem through a model necessarily involves the introduction of parameters. Hence, one wishes to have a solution of the problem that is a function of all these parameters: a parametric solution. However, the construction of such parametric solutions exhibiting localization in space is only ensured by costly and time-consuming tests, which can be both numerical or experimental. Numerical methodologies used classically imply enormous computational efforts for exploring the design space. Therefore, parametric solutions obtained using advanced nonlinear regressions are an essential tool to address this challenge. However, classical regression techniques, even the most advanced ones, can lead to non physical interpolation in some fields such as fluid dynamics, where the solution localizes in different regions depending on the problem parameters choice. In this context, Optimal Transport (OT) offers a mathematical approach to measure distances and interpolate between general objects in a, sometimes, more physical way than the classical interpolation approach. Thus, OT has become fundamental in some fields such as statistics or computer vision, and it is being increasingly used in fields such as computational mechanics. However, the OT problem is usually computationally costly to solve and not adapted to be accessed in an online manner. Therefore, the aim of this paper is combining advanced nonlinear regressions with Optimal Transport in order to implement a parametric real-time model based on OT. To this purpose, a parametric model is built offline relying on Model Order Reduction and OT, leading to a real-time interpolation tool following Optimal Transport theory. Such a tool is of major interest in design processes, but also within the digital twin rationale. Tue, 30 Nov 2021 00:00:00 GMT http://hdl.handle.net/10985/22204 2021-11-30T00:00:00Z TORREGROSA, Sergio CHAMPANEY, Victor AMMAR, Amine HERBERT, Vincent CHINESTA SORIA, Francisco The description of a physical problem through a model necessarily involves the introduction of parameters. Hence, one wishes to have a solution of the problem that is a function of all these parameters: a parametric solution. However, the construction of such parametric solutions exhibiting localization in space is only ensured by costly and time-consuming tests, which can be both numerical or experimental. Numerical methodologies used classically imply enormous computational efforts for exploring the design space. Therefore, parametric solutions obtained using advanced nonlinear regressions are an essential tool to address this challenge. However, classical regression techniques, even the most advanced ones, can lead to non physical interpolation in some fields such as fluid dynamics, where the solution localizes in different regions depending on the problem parameters choice. In this context, Optimal Transport (OT) offers a mathematical approach to measure distances and interpolate between general objects in a, sometimes, more physical way than the classical interpolation approach. Thus, OT has become fundamental in some fields such as statistics or computer vision, and it is being increasingly used in fields such as computational mechanics. However, the OT problem is usually computationally costly to solve and not adapted to be accessed in an online manner. Therefore, the aim of this paper is combining advanced nonlinear regressions with Optimal Transport in order to implement a parametric real-time model based on OT. To this purpose, a parametric model is built offline relying on Model Order Reduction and OT, leading to a real-time interpolation tool following Optimal Transport theory. Such a tool is of major interest in design processes, but also within the digital twin rationale. http://hdl.handle.net/10985/20468 Learning the Parametric Transfer Function of Unitary Operations for Real-Time Evaluation of Manufacturing Processes Involving Operations Sequencing LOREAU, Tanguy; CHAMPANEY, Victor; HASCOËT, Nicolas; MOURGUE, Philippe; DUVAL, Jean-Louis; CHINESTA SORIA, Francisco For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10985/20468 2021-01-01T00:00:00Z LOREAU, Tanguy CHAMPANEY, Victor HASCOËT, Nicolas MOURGUE, Philippe DUVAL, Jean-Louis CHINESTA SORIA, Francisco For better designing manufacturing processes, surrogate models were widely considered in the past, where the effect of different material and process parameters was considered from the use of a parametric solution. The last contains the solution of the model describing the system under study, for any choice of the selected parameters. These surrogate models, also known as meta-models, virtual charts or computational vademecum, in the context of model order reduction, were successfully employed in a variety of industrial applications. However, they remain confronted to a major difficulty when the number of parameters grows exponentially. Thus, processes involving trajectories or sequencing entail a combinatorial exposition (curse of dimensionality) not only due to the number of possible combinations, but due to the number of parameters needed to describe the process. The present paper proposes a promising route for circumventing, or at least alleviating that difficulty. The proposed technique consists of a parametric transfer function that, as soon as it is learned, allows for, from a given state, inferring the new state after the application of a unitary operation, defined as a step in the sequenced process. Thus, any sequencing can be evaluated almost in real time by chaining that unitary transfer function, whose output becomes the input of the next operation. The benefits and potential of such a technique are illustrated on a problem of industrial relevance, the one concerning the induced deformation on a structural part when printing on it a series of stiffeners.