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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 25 Feb 2024 12:10:14 GMT2024-02-25T12:10:14ZA 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
IBAÑEZ, Ruben; 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/10985/166762018-01-01T00:00:00ZIBAÑEZ, RubenABISSET-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.On the data-driven modeling of reactive extrusion
http://hdl.handle.net/10985/19137
On the data-driven modeling of reactive extrusion
IBAÑEZ, Ruben; CASTERAN, Fanny; ARGERICH, Clara; GHNATIOS, Chady; HASCOET, Nicolas; AMMAR, Amine; CASSAGNAU, Philippe; CHINESTA, Francisco
This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10985/191372020-01-01T00:00:00ZIBAÑEZ, RubenCASTERAN, FannyARGERICH, ClaraGHNATIOS, ChadyHASCOET, NicolasAMMAR, AmineCASSAGNAU, PhilippeCHINESTA, FranciscoThis paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.A local multiple proper generalized decomposition based on the partition of unity
http://hdl.handle.net/10985/17949
A local multiple proper generalized decomposition based on the partition of unity
IBAÑEZ, Ruben; ABISSET-CHAVANNE, Emmanuelle; CHINESTA, Francisco; HUERTA, Antonio; CUETO, Elías G.
It is well known that model order reduction techniques that project the solution of the problem at hand onto a low-dimensional subspace present difficulties when this solution lies on a nonlinear manifold. To overcome these difficulties (notably, an undesirable increase in the number of required modes in the solution), several solutions have been suggested. Among them, we can cite the use of nonlinear dimensionality reduction techniques or, alternatively, the employ of linear local reduced order approaches. These last approaches usually present the difficulty of ensuring continuity between these local models. Here, a new method is presented, which ensures this continuity by resorting to the paradigm of the partition of unity while employing proper generalized decompositions at each local patch.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/179492019-01-01T00:00:00ZIBAÑEZ, RubenABISSET-CHAVANNE, EmmanuelleCHINESTA, FranciscoHUERTA, AntonioCUETO, Elías G.It is well known that model order reduction techniques that project the solution of the problem at hand onto a low-dimensional subspace present difficulties when this solution lies on a nonlinear manifold. To overcome these difficulties (notably, an undesirable increase in the number of required modes in the solution), several solutions have been suggested. Among them, we can cite the use of nonlinear dimensionality reduction techniques or, alternatively, the employ of linear local reduced order approaches. These last approaches usually present the difficulty of ensuring continuity between these local models. Here, a new method is presented, which ensures this continuity by resorting to the paradigm of the partition of unity while employing proper generalized decompositions at each local patch.Some 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
ABISSET-CHAVANNE, Emmanuelle; CUETO, Elías G.; AMMAR, Amine; DUVAL, Jean Louis; CHINESTA, Francisco; IBAÑEZ, Ruben
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/10985/176162019-01-01T00:00:00ZABISSET-CHAVANNE, EmmanuelleCUETO, Elías G.AMMAR, AmineDUVAL, Jean LouisCHINESTA, FranciscoIBAÑEZ, RubenCompressed 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.