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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Fri, 14 Aug 2020 14:31:30 GMT2020-08-14T14:31:30ZSome 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.A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
http://hdl.handle.net/10985/16676
A Multidimensional Data-Driven Sparse Identification Technique: The Sparse Proper Generalized Decomposition
IBÁÑEZ, Rubén; ABISSET-CHAVANNE, Emmanuelle; AMMAR, Amine; GONZALEZ, David; CUETO, Elias; HUERTA, Antonio; DUVAL, Jean Louis; CHINESTA, Francisco
Sparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/166762018-01-01T00:00:00ZIBÁÑEZ, RubénABISSET-CHAVANNE, EmmanuelleAMMAR, AmineGONZALEZ, DavidCUETO, EliasHUERTA, AntonioDUVAL, Jean LouisCHINESTA, FranciscoSparse model identification by means of data is especially cumbersome if the sought dynamics live in a high dimensional space. This usually involves the need for large amount of data, unfeasible in such a high dimensional settings. This well-known phenomenon, coined as the curse of dimensionality, is here overcome by means of the use of separate representations. We present a technique based on the same principles of the Proper Generalized Decomposition that enables the identification of complex laws in the low-data limit. We provide examples on the performance of the technique in up to ten dimensions.From linear to nonlinear PGD-based parametric structural dynamics
http://hdl.handle.net/10985/15463
From linear to nonlinear PGD-based parametric structural dynamics
QUARANTA, Giacomo; ARGERICH MARTIN, Clara; IBÁÑEZ, Rubén; DUVAL, Jean Louis; CUETO, Elias; CHINESTA, Francisco
The present paper analyzes different integration schemes of solid dynamics in the frequency domain involving the so-called Proper Generalized Decomposition – PGD. The last framework assumes for the solution a parametric dependency with respect to frequency. This procedure allowed introducing other parametric dependences related to loading, geometry, and material properties. However, in these cases, affine decompositions are required for an efficient computation of separated representations. A possibility for circumventing such difficulty consists in combining modal and harmonic analysis for defining an hybrid integration scheme. Moreover, such a procedure, as proved in the present work, can be easily generalized to address nonlinear parametric dynamics, as well as to solve problems with non-symmetric stiffness matrices, always operating in the domain of low frequencies.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/154632019-01-01T00:00:00ZQUARANTA, GiacomoARGERICH MARTIN, ClaraIBÁÑEZ, RubénDUVAL, Jean LouisCUETO, EliasCHINESTA, FranciscoThe present paper analyzes different integration schemes of solid dynamics in the frequency domain involving the so-called Proper Generalized Decomposition – PGD. The last framework assumes for the solution a parametric dependency with respect to frequency. This procedure allowed introducing other parametric dependences related to loading, geometry, and material properties. However, in these cases, affine decompositions are required for an efficient computation of separated representations. A possibility for circumventing such difficulty consists in combining modal and harmonic analysis for defining an hybrid integration scheme. Moreover, such a procedure, as proved in the present work, can be easily generalized to address nonlinear parametric dynamics, as well as to solve problems with non-symmetric stiffness matrices, always operating in the domain of low frequencies.On the coupling of local 3D solutions and global 2D shell theory in structural mechanics
http://hdl.handle.net/10985/14597
On the coupling of local 3D solutions and global 2D shell theory in structural mechanics
QUARANTA, Giacomo; ZIANE, Mustapha; ABISSET-CHAVANNE, Emmanuelle; DUVAL, Jean Louis; CHINESTA, Francisco; ESI GROUP
Most of mechanical systems and complex structures exhibit plate and shell components. Therefore, 2D simulation, based on plate and shell theory, appears as an appealing choice in structural analysis as it allows reducing the computational complexity. Nevertheless, this 2D framework fails for capturing rich physics compromising the usual hypotheses considered when deriving standard plate and shell theories. To circumvent, or at least alleviate this issue, authors proposed in their former works an in-plane-out-of-plane separated representation able to capture rich 3D behaviors while keeping the computational complexity of 2D simulations. However, that procedure it was revealed to be too intrusive for being introduced into existing commercial softwares. Moreover, experience indicated that such enriched descriptions are only compulsory locally, in some regions or structure components. In the present paper we propose an enrichment procedure able to address 3D local behaviors, preserving the direct minimally-invasive coupling with existing plate and shell discretizations. The proposed strategy will be extended to inelastic behaviors and structural dynamics.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/145972019-01-01T00:00:00ZQUARANTA, GiacomoZIANE, MustaphaABISSET-CHAVANNE, EmmanuelleDUVAL, Jean LouisCHINESTA, FranciscoESI GROUPMost of mechanical systems and complex structures exhibit plate and shell components. Therefore, 2D simulation, based on plate and shell theory, appears as an appealing choice in structural analysis as it allows reducing the computational complexity. Nevertheless, this 2D framework fails for capturing rich physics compromising the usual hypotheses considered when deriving standard plate and shell theories. To circumvent, or at least alleviate this issue, authors proposed in their former works an in-plane-out-of-plane separated representation able to capture rich 3D behaviors while keeping the computational complexity of 2D simulations. However, that procedure it was revealed to be too intrusive for being introduced into existing commercial softwares. Moreover, experience indicated that such enriched descriptions are only compulsory locally, in some regions or structure components. In the present paper we propose an enrichment procedure able to address 3D local behaviors, preserving the direct minimally-invasive coupling with existing plate and shell discretizations. The proposed strategy will be extended to inelastic behaviors and structural dynamics.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.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.