Showing papers on "Applied science published in 2019"
TL;DR: A method able to avoid the identification of the constitutive equations of complex systems and rather work in a purely numerical manner by employing experimental data that is able to identify both the Hamiltonian and dissipative parts of the dynamics while satisfying fundamental laws such as energy conservation or positive production of entropy.
Abstract: In the paradigm of data-intensive science, automated, unsupervised discovering of governing equations for a given physical phenomenon has attracted a lot of attention in several branches of applied sciences. In this work, we propose a method able to avoid the identification of the constitutive equations of complex systems and rather work in a purely numerical manner by employing experimental data. In sharp contrast to most existing techniques, this method does not rely on the assumption on any particular form for the model (other than some fundamental restrictions placed by classical physics such as the second law of thermodynamics, for instance) nor forces the algorithm to find among a predefined set of operators those whose predictions fit best to the available data. Instead, the method is able to identify both the Hamiltonian (conservative) and dissipative parts of the dynamics while satisfying fundamental laws such as energy conservation or positive production of entropy, for instance. The proposed method is tested against some examples of discrete as well as continuum mechanics, whose accurate results demonstrate the validity of the proposed approach.
76 citations
TL;DR: This work focuses on model-driven engineering practices to enable replicable and reusable simulation experiments and proposes that crucial and labor intensive parts of simulation experiments could be supported by model transformations.
Abstract: Simulation experiments are an essential part of computational science and engineering. The use of simulation models is widely adopted by practitioners from diverse areas of applied sciences. Nevert...
6 citations
Dissertation•
01 May 2019
TL;DR: In this paper, a thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science (M.S. in 2019) was described, which was submitted to the Faculty of Natural and Applied Sciences at Notre Dame University.
Abstract: "A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science". M.S.; -- Faculty of Natural and Applied Sciences, Department of Computer Science, Notre Dame University, Louaize, 2019; Includes bibliographical references (leaves 38-42).
3 citations
Dissertation•
01 May 2019
TL;DR: "A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science"; M.S.
Abstract: "A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science"; M.S. -- Faculty of Natural and Applied Sciences, Department of Computer Science, Notre Dame University, Louaize, 2019; Includes bibliographical references (leaves 56-59).
2 citations