Results 21 to 30 of about 19,419,888 (317)
Data-Driven Control: theory and applications [PDF]
, 2022 openLo scopo di questa tesi è studiare le tecniche di Data-Driven Control, in particolare viene studiato il problema di controllo attraverso il Data Driven Predictve Control.
OLIVIERO, ALESSANDRA
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Replication data for: Brillouin zone folding driven bound states in the continuum
, 2023 This dataset contains all the data used in the paper, titled 'Brillouin zone folding driven bound states in the continuum'
Srivastava, Yogesh Kumar +4 more
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Data-Driven Problems in Elasticity [PDF]
Archive for Rational Mechanics and Analysis, 2018 Result now covers the two well problem in full generality. Proof simplified.
Conti, S., Müller, S., Ortiz, M.
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Data democratization: Empowering employees for data-driven innovation
, 2023 The exponentially growing usage and its benefits of digitizing data, as well as changes in data management practices, are continuously moderating the global economy and how organizations perform business operations.
Lokuge, Sachithra, Samarasinghe, Sasari
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IEEE Access, 2018 In this paper, a barrier Lyapunov function (BLF)-based backstepping control design is proposed for uncertain rigid spacecraft with both input and output constraints.
Zhongtian Chen +3 more
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Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
Applied Sciences, 2022 The avoidance of scrap and the adherence to tolerances is an important goal in manufacturing. This requires a good engineering understanding of the underlying process. To achieve this, real physical experiments can be conducted.
Johannes G. Hoffer +2 more
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ETNA - Electronic Transactions on Numerical Analysis, 2022 We present a framework enabling variational data assimilation for gradient flows in general metric spaces, based on the minimizing movement (or Jordan-Kinderlehrer-Otto) approximation scheme. After discussing stability properties in the most general case, we specialise to the space of probability measures endowed with the Wasserstein distance.
Pietschmann, Jan-Frederik +1 more
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Self-supervised optimization of random material microstructures in the small-data regime
npj Computational Materials, 2022 While the forward and backward modeling of the process-structure-property chain has received a lot of attention from the materials’ community, fewer efforts have taken into consideration uncertainties.
Maximilian Rixner +1 more
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Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks
IEEE Access, 2023 Physics-informed neural networks (PINNs) have emerged as a promising deep learning method, capable of solving forward and inverse problems governed by differential equations.
Franz M. Rohrhofer +3 more
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Data-Driven Innovation: What Is It? [PDF]
IEEE Transactions on Engineering Management, 2021 The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized ...
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