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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 11 Apr 2021 10:07:11 GMT2021-04-11T10:07:11ZSome applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
http://hdl.handle.net/10985/17616
Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
IBAÑEZ, R.; ABISSET-CHAVANNE, Emmanuelle; CUETO, Elías G.; AMMAR, Amine; DUVAL, Jean Louis; CHINESTA, Francisco
Compressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to reconstruct a signal at less than 2Q samplings per second, where Q stands for the highest frequency content of the signal. This property has, however, important applications in the field of computational mechanics, as we analyze in this paper. We consider a wide variety of applications, such as model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Examples are provided for all of them that show the potentialities of compressed sensing in terms of CPU savings in the field of computational mechanics.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/176162019-01-01T00:00:00ZIBAÑEZ, R.ABISSET-CHAVANNE, EmmanuelleCUETO, Elías G.AMMAR, AmineDUVAL, Jean LouisCHINESTA, FranciscoCompressed sensing is a signal compression technique with very remarkable properties. Among them, maybe the most salient one is its ability of overcoming the Shannon–Nyquist sampling theorem. In other words, it is able to reconstruct a signal at less than 2Q samplings per second, where Q stands for the highest frequency content of the signal. This property has, however, important applications in the field of computational mechanics, as we analyze in this paper. We consider a wide variety of applications, such as model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction. Examples are provided for all of them that show the potentialities of compressed sensing in terms of CPU savings in the field of computational mechanics.Parametric evaluation of part distortion in additive manufacturing processes
http://hdl.handle.net/10985/18364
Parametric evaluation of part distortion in additive manufacturing processes
QUARANTA, Giacomo; HAUG, Eberhard; DUVAL, Jean Louis; CHINESTA, Francisco
Additive manufacturing is the more and more considered in industry, however efficient simulation tools able to perform accurate predictions are still quite limited. The main difficulties for an efficient simulation are related to the multiple scales, the multiple and complex physics involved, as well as the strong dependency on the process trajectory. This paper aims at proposing a simplified parametric modeling and its subsequent parametric solution for evaluating parametrically the manufactured part distortion. The involved parameter are the ones parametrizing the process trajectories, the thermal shrinkage intensity and anisotropy (the former depending on several material and process parameters and the last directly depending on the process trajectory) and the deposited layers. The resulting simulation tool allows evaluating in real-time the impact of the parameters just referred on the part distortion, and proceed to the required geometrical compensation
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/183642020-01-01T00:00:00ZQUARANTA, GiacomoHAUG, EberhardDUVAL, Jean LouisCHINESTA, FranciscoAdditive manufacturing is the more and more considered in industry, however efficient simulation tools able to perform accurate predictions are still quite limited. The main difficulties for an efficient simulation are related to the multiple scales, the multiple and complex physics involved, as well as the strong dependency on the process trajectory. This paper aims at proposing a simplified parametric modeling and its subsequent parametric solution for evaluating parametrically the manufactured part distortion. The involved parameter are the ones parametrizing the process trajectories, the thermal shrinkage intensity and anisotropy (the former depending on several material and process parameters and the last directly depending on the process trajectory) and the deposited layers. The resulting simulation tool allows evaluating in real-time the impact of the parameters just referred on the part distortion, and proceed to the required geometrical compensationMultiscale proper generalized decomposition based on the partition of unity
http://hdl.handle.net/10985/18456
Multiscale proper generalized decomposition based on the partition of unity
IBÁÑEZ PINILLO, Rubén; AMMAR, Amine; CUETO, Elías G.; HUERTA, Antonio; DUVAL, Jean Louis; CHINESTA, Francisco
Solutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The proposed methodology is based on the standard proper generalized decomposition rationale; thus, the PDE is transformed into a nonlinear system that iterates between microscale and macroscale states, where the time coordinate could be viewed as a 2D time, representing the microtime and macrotime scales. The macroscale effects are taken into account because of an FEM-based macrodiscretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the domain. The proposed methodology can be seen as an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/184562019-01-01T00:00:00ZIBÁÑEZ PINILLO, RubénAMMAR, AmineCUETO, Elías G.HUERTA, AntonioDUVAL, Jean LouisCHINESTA, FranciscoSolutions of partial differential equations could exhibit a multiscale behavior. Standard discretization techniques are constraints to mesh up to the finest scale to predict accurately the response of the system. The proposed methodology is based on the standard proper generalized decomposition rationale; thus, the PDE is transformed into a nonlinear system that iterates between microscale and macroscale states, where the time coordinate could be viewed as a 2D time, representing the microtime and macrotime scales. The macroscale effects are taken into account because of an FEM-based macrodiscretization, whereas the microscale effects are handled with unidimensional parent spaces that are replicated throughout the domain. The proposed methodology can be seen as an alternative route to circumvent prohibitive meshes arising from the necessity of capturing fine-scale behaviors.Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
http://hdl.handle.net/10985/18539
Incremental dynamic mode decomposition: A reduced-model learner operating at the low-data limit
REILLE, Agathe; HASCOËT, Nicolas; GHNATIOS, Chady; AMMAR, Amine; CUETO, Elías G.; DUVAL, Jean Louis; CHINESTA, Francisco; KEUNINGS, Roland
The present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/185392019-01-01T00:00:00ZREILLE, AgatheHASCOËT, NicolasGHNATIOS, ChadyAMMAR, AmineCUETO, Elías G.DUVAL, Jean LouisCHINESTA, FranciscoKEUNINGS, RolandThe present work aims at proposing a new methodology for learning reduced models from a small amount of data. It is based on the fact that discrete models, or their transfer function counterparts, have a low rank and then they can be expressed very efficiently using few terms of a tensor decomposition. An efficient procedure is proposed as well as a way for extending it to nonlinear settings while keeping limited the impact of data noise. The proposed methodology is then validated by considering a nonlinear elastic problem and constructing the model relating tractions and displacements at the observation points.Structural health monitoring by combining machine learning and dimensionality reduction techniques
http://hdl.handle.net/10985/15522
Structural health monitoring by combining machine learning and dimensionality reduction techniques
QUARANTA, Giacomo; LOPEZ, Elena; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; HUERTA, Antonio; CHINESTA, Francisco
Structural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/155222019-01-01T00:00:00ZQUARANTA, GiacomoLOPEZ, ElenaABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisHUERTA, AntonioCHINESTA, FranciscoStructural Health Monitoring is of major interest in many areas of structural mechanics. This paper presents a new approach based on the combination of dimensionality reduction and data-mining techniques able to differentiate damaged and undamaged regions in a given structure. Indeed, existence, severity (size) and location of damage can be efficiently estimated from collected data at some locations from which the fields of interest are completed before the analysis based on machine learning and dimensionality reduction techniques proceed.Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data
http://hdl.handle.net/10985/16796
Virtual, Digital and Hybrid Twins: A New Paradigm in Data-Based Engineering and Engineered Data
CHINESTA, Francisco; CUETO, Elías G.; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; KHALDI, Fouad El
Engineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/167962018-01-01T00:00:00ZCHINESTA, FranciscoCUETO, Elías G.ABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisKHALDI, Fouad ElEngineering is evolving in the same way than society is doing. Nowadays, data is acquiring a prominence never imagined. In the past, in the domain of materials, processes and structures, testing machines allowed extract data that served in turn to calibrate state-of-the-art models. Some calibration procedures were even integrated within these testing machines. Thus, once the model had been calibrated, computer simulation takes place. However, data can offer much more than a simple state-of-the-art model calibration, and not only from its simple statistical analysis, but from the modeling and simulation viewpoints. This gives rise to the the family of so-called twins: the virtual, the digital and the hybrid twins. Moreover, as discussed in the present paper, not only data serve to enrich physically-based models. These could allow us to perform a tremendous leap forward, by replacing big-data-based habits by the incipient smart-data paradigm.Advanced modeling and simulation of sheet moulding compound (SMC) processes
http://hdl.handle.net/10985/19143
Advanced modeling and simulation of sheet moulding compound (SMC) processes
PEREZ, Marta; PRONO, David; GHNATIOS, Chady; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; CHINESTA, Francisco
In SMC processes, a charge of a composite material, which typically consists of a matrix composed of an unsaturated polyester or vinylester, reinforced with chopped glass fibres or carbon fi bre bundles and fillers, is placed on the bottom half of the preheated mould. The charge usually covers 30 to 90% of the total area. The upper half of the mould is closed rapidly at a speed of about 40 mm/s. This rapid movement causes the charge to flow inside the cavity. The reinforcing fibres are carried by the resin and experience a change of confi guration during the flow. This strongly influences the mechanical properties of the final part. Several issues compromises its efficient numerical simulation, among them: (i) the modeling of flow kinematics able to induce eventual fibres/resin segregation, (ii) the con ned fibres orientation evolution and its accurate prediction, (iii) local dilution effects, (iv) flow bifurcation at junctions and its impact on the fibres orientation state, (v) charge / mould contact and (vi) parametric solutions involving non-interpolative fields. The present paper reports advanced modeling and simulation techniques for circumventing, or at least alleviating, the just referred difficulties.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/191432019-01-01T00:00:00ZPEREZ, MartaPRONO, DavidGHNATIOS, ChadyABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisCHINESTA, FranciscoIn SMC processes, a charge of a composite material, which typically consists of a matrix composed of an unsaturated polyester or vinylester, reinforced with chopped glass fibres or carbon fi bre bundles and fillers, is placed on the bottom half of the preheated mould. The charge usually covers 30 to 90% of the total area. The upper half of the mould is closed rapidly at a speed of about 40 mm/s. This rapid movement causes the charge to flow inside the cavity. The reinforcing fibres are carried by the resin and experience a change of confi guration during the flow. This strongly influences the mechanical properties of the final part. Several issues compromises its efficient numerical simulation, among them: (i) the modeling of flow kinematics able to induce eventual fibres/resin segregation, (ii) the con ned fibres orientation evolution and its accurate prediction, (iii) local dilution effects, (iv) flow bifurcation at junctions and its impact on the fibres orientation state, (v) charge / mould contact and (vi) parametric solutions involving non-interpolative fields. The present paper reports advanced modeling and simulation techniques for circumventing, or at least alleviating, the just referred difficulties.Non-intrusive proper generalized decomposition involving space and parameters: application to the mechanical modeling of 3D woven fabrics
http://hdl.handle.net/10985/18457
Non-intrusive proper generalized decomposition involving space and parameters: application to the mechanical modeling of 3D woven fabrics
LEÓN, Angel; MUELLER, SEBASTIEN; DE LUCA, Patrick; SAID, Rajab; DUVAL, Jean Louis; CHINESTA, Francisco
In our former works we proposed different Model Order Reduction strategies for alleviating the complexity of computational simulations. In fact we proved that separated representations are specially appealing for addressing many issues, in particular, the treatment of 3D models defined in degenerated domains (those involving very different characteristic dimensions, like beams, plate and shells) as well as the solution of parametrized models for calculating their parametric solutions. However it was proved that the efficiency of solvers based on the construction of such separated representations strongly depends on the affine decompositions (separability) of operators, parameters and geometry. Even if our works proved that different techniques exists for performing such beneficial separation prior of applying the separated representation constructor, the complexity of the solver increases in certain circumstances too much, as the one involving the space separation of complex microstructures concerned by 3D woven fabrics. In this paper we explore an alternative route that allows circumventing the just referred difficulties. Thus, instead of following the standard procedure that consists of introducing the separated representation of the unknown field prior to discretize the models, the strategy here proposed consists of proceeding inversely: first the model is discretized and then the separated representation of the discrete unknown field is enforced. Such a procedure enables the consideration of very complex and non separable features, like complex domains, boundary conditions and microstructures as the ones concerned by homogenized models of complex and rich 3D woven fabrics. It will be proved that such a procedure can be also easily coupled with a non-intrusive treatment of the parametric dimensions by using a sparse hierarchical collocation technique.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/184572019-01-01T00:00:00ZLEÓN, AngelMUELLER, SEBASTIENDE LUCA, PatrickSAID, RajabDUVAL, Jean LouisCHINESTA, FranciscoIn our former works we proposed different Model Order Reduction strategies for alleviating the complexity of computational simulations. In fact we proved that separated representations are specially appealing for addressing many issues, in particular, the treatment of 3D models defined in degenerated domains (those involving very different characteristic dimensions, like beams, plate and shells) as well as the solution of parametrized models for calculating their parametric solutions. However it was proved that the efficiency of solvers based on the construction of such separated representations strongly depends on the affine decompositions (separability) of operators, parameters and geometry. Even if our works proved that different techniques exists for performing such beneficial separation prior of applying the separated representation constructor, the complexity of the solver increases in certain circumstances too much, as the one involving the space separation of complex microstructures concerned by 3D woven fabrics. In this paper we explore an alternative route that allows circumventing the just referred difficulties. Thus, instead of following the standard procedure that consists of introducing the separated representation of the unknown field prior to discretize the models, the strategy here proposed consists of proceeding inversely: first the model is discretized and then the separated representation of the discrete unknown field is enforced. Such a procedure enables the consideration of very complex and non separable features, like complex domains, boundary conditions and microstructures as the ones concerned by homogenized models of complex and rich 3D woven fabrics. It will be proved that such a procedure can be also easily coupled with a non-intrusive treatment of the parametric dimensions by using a sparse hierarchical collocation technique.Nonlinear Regression Operating on Microstructures Described from Topological Data Analysis for the Real-Time Prediction of Effective Properties
http://hdl.handle.net/10985/18955
Nonlinear Regression Operating on Microstructures Described from Topological Data Analysis for the Real-Time Prediction of Effective Properties
YUN, Minyoung; ARGERICH, Clara; CUETO, Elias; DUVAL, Jean Louis; CHINESTA, Francisco
Real-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/189552020-01-01T00:00:00ZYUN, MinyoungARGERICH, ClaraCUETO, EliasDUVAL, Jean LouisCHINESTA, FranciscoReal-time decision making needs evaluating quantities of interest (QoI) in almost real time. When these QoI are related to models based on physics, the use of Model Order Reduction techniques allows speeding-up calculations, enabling fast and accurate evaluations. To accommodate real-time constraints, a valuable route consists of computing parametric solutions—the so-called computational vademecums—that constructed off-line, can be inspected on-line. However, when dealing with shapes and topologies (complex or rich microstructures) their parametric description constitutes a major difficulty. In this paper, we propose using Topological Data Analysis for describing those rich topologies and morphologies in a concise way, and then using the associated topological descriptions for generating accurate supervised classification and nonlinear regression, enabling an almost real-time evaluation of QoI and the associated decision making.Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
http://hdl.handle.net/10985/17438
Hybrid constitutive modeling: data-driven learning of corrections to plasticity models
IBÁÑEZ, Rubén; ABISSET-CHAVANNE, Emmanuelle; GONZÁLEZ, David; DUVAL, Jean Louis; CUETO, Elias; CHINESTA, Francisco
In recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/174382019-01-01T00:00:00ZIBÁÑEZ, RubénABISSET-CHAVANNE, EmmanuelleGONZÁLEZ, DavidDUVAL, Jean LouisCUETO, EliasCHINESTA, FranciscoIn recent times a growing interest has arose on the development of data-driven techniques to avoid the employ of phenomenological constitutive models. While it is true that, in general, data do not fit perfectly to existing models, and present deviations from the most popular ones, we believe that this does not justify (or, at least, not always) to abandon completely all the acquired knowledge on the constitutive characterization of materials. Instead, what we propose here is, by means of machine learning techniques, to develop correction to those popular models so as to minimize the errors in constitutive modeling.