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The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 03 Mar 2024 16:07:51 GMT2024-03-03T16:07:51ZComplex Algorithms for Data-Driven Model Learning in Science and Engineering
http://hdl.handle.net/10985/20555
Complex Algorithms for Data-Driven Model Learning in Science and Engineering
MONTÁNS, Francisco J.; CHINESTA, Francisco; GÓMEZ-BOMBARELLI, Rafael; KUTZ, J. Nathan
no abstract
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/205552019-01-01T00:00:00ZMONTÁNS, Francisco J.CHINESTA, FranciscoGÓMEZ-BOMBARELLI, RafaelKUTZ, J. Nathanno abstractCrossing Scales: Data-Driven Determination of the Micro-scale Behavior of Polymers From Non-homogeneous Tests at the Continuum-Scale
http://hdl.handle.net/10985/22235
Crossing Scales: Data-Driven Determination of the Micro-scale Behavior of Polymers From Non-homogeneous Tests at the Continuum-Scale
AMORES, Víctor J.; MONTÁNS, Francisco J.; CUETO, Elías; CHINESTA, Francisco
We propose an efficient method to determine the micro-structural entropic behavior of polymer chains directly from a sufficiently rich non-homogeneous experiment at the continuum scale. The procedure is developed in 2 stages: First, a Macro-Micro-Macro approach; second, a finite element method. Thus, we no longer require the typical stress-strain curves from standard homogeneous tests, but we use instead the applied/reaction forces and the displacement field obtained, for example, from Digital Image Correlation. The approach is based on the P-spline local approximation of the constituents behavior at the micro-scale (a priori unknown). The sought spline vertices determining the polymer behavior are first pushed up from the micro-scale to the integration point of the finite element, and then from the integration point to the element forces. The polymer chain behavior is then obtained immediately by solving a linear system of equations which results from a least squares minimization error, resulting in an inverse problem which crosses material scales. The result is physically interpretable and directly linked to the micro-structure of the material, and the resulting polymer behavior may be employed in any other finite element simulation. We give some demonstrative examples (academic and from actual polymers) in which we demonstrate that we are capable of recovering “unknown” analytical models and spline-based constitutive behavior previously obtained from homogeneous tests.
Sun, 01 May 2022 00:00:00 GMThttp://hdl.handle.net/10985/222352022-05-01T00:00:00ZAMORES, Víctor J.MONTÁNS, Francisco J.CUETO, ElíasCHINESTA, FranciscoWe propose an efficient method to determine the micro-structural entropic behavior of polymer chains directly from a sufficiently rich non-homogeneous experiment at the continuum scale. The procedure is developed in 2 stages: First, a Macro-Micro-Macro approach; second, a finite element method. Thus, we no longer require the typical stress-strain curves from standard homogeneous tests, but we use instead the applied/reaction forces and the displacement field obtained, for example, from Digital Image Correlation. The approach is based on the P-spline local approximation of the constituents behavior at the micro-scale (a priori unknown). The sought spline vertices determining the polymer behavior are first pushed up from the micro-scale to the integration point of the finite element, and then from the integration point to the element forces. The polymer chain behavior is then obtained immediately by solving a linear system of equations which results from a least squares minimization error, resulting in an inverse problem which crosses material scales. The result is physically interpretable and directly linked to the micro-structure of the material, and the resulting polymer behavior may be employed in any other finite element simulation. We give some demonstrative examples (academic and from actual polymers) in which we demonstrate that we are capable of recovering “unknown” analytical models and spline-based constitutive behavior previously obtained from homogeneous tests.Data-driven modeling and learning in science and engineering
http://hdl.handle.net/10985/19480
Data-driven modeling and learning in science and engineering
MONTÁNS, Francisco J.; CHINESTA, Francisco; GÓMEZ-BOMBARELLI, Rafael; KUTZ, J. Nathan
In the past, data in which science and engineering is based, was scarce and frequently obtained by experiments proposed to verify a given hypothesis. Each experiment was able to yield only very limited data. Today, data is abundant and abundantly collected in each single experiment at a very small cost. Data-driven modeling and scientific discovery is a change of paradigm on how many problems, both in science and engineering, are addressed. Some scientific fields have been using artificial intelligence for some time due to the inherent difficulty in obtaining laws and equations to describe some phenomena. However, today data-driven approaches are also flooding fields like mechanics and materials science, where the traditional approach seemed to be highly satisfactory. In this paper we review the application of data-driven modeling and model learning procedures to different fields in science and engineering.
FJM acknowledges support from Agencia Estatal de Investigación of Spain, grant PGC-2018-097257-B-C32. JNK acknowl-edges support from the Air Force Oﬃce of Scientiﬁc Research (AFOSR) grant FA9550-17-1-0329.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10985/194802019-01-01T00:00:00ZMONTÁNS, Francisco J.CHINESTA, FranciscoGÓMEZ-BOMBARELLI, RafaelKUTZ, J. NathanIn the past, data in which science and engineering is based, was scarce and frequently obtained by experiments proposed to verify a given hypothesis. Each experiment was able to yield only very limited data. Today, data is abundant and abundantly collected in each single experiment at a very small cost. Data-driven modeling and scientific discovery is a change of paradigm on how many problems, both in science and engineering, are addressed. Some scientific fields have been using artificial intelligence for some time due to the inherent difficulty in obtaining laws and equations to describe some phenomena. However, today data-driven approaches are also flooding fields like mechanics and materials science, where the traditional approach seemed to be highly satisfactory. In this paper we review the application of data-driven modeling and model learning procedures to different fields in science and engineering.