Non-reciprocal wave propagation in modulated elastic metamaterialsProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences - Tập 473 Số 2202 - Trang 20170188 - 2017
Hussein Nassar, Huan Chen, Andrew N. Norris, Michael R. Haberman, Guoliang Huang
Time-reversal symmetry for elastic wave propagation breaks down in a resonant mass-in-mass lattice whose inner-stiffness is weakly modulated in space and in time in a wave-like fashion. Specifically, one-way wave transmission, conversion and amplification as well as unidirectional wave blocking are demonstrated analytically through an asymptotic analysis based on coupled mode theory and nu...... hiện toàn bộ
A theorem on the existence of trace-form generalized entropiesProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences - Tập 471 Số 2183 - Trang 20150165 - 2015
Piergiulio Tempesta
An analytic technique is proposed, which allows to generate many new examples of entropic functionals generalizing the standard Boltzmann–Gibbs entropy. Our approach is based on the existence of a group-theoretical structure, which is intimately related with the notion of entropy, as clarified in recent work of the author. The new entropies proposed satisfy the first three Shannon–Khinchin...... hiện toàn bộ
A depth-averaged debris-flow model that includes the effects of evolving dilatancy. II. Numerical predictions and experimental testsProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences - Tập 470 Số 2170 - Trang 20130820 - 2014
David L. George, Richard M. Iverson
We evaluate a new depth-averaged mathematical model that is designed to simulate all stages of debris-flow motion, from initiation to deposition. A companion paper shows how the model's five governing equations describe simultaneous evolution of flow thickness, solid volume fraction, basal pore-fluid pressure and two components of flow momentum. Each equation contains a source term that re...... hiện toàn bộ
Sparse identification of nonlinear dynamics for model predictive control in the low-data limitProceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences - Tập 474 Số 2219 - Trang 20180335 - 2018
Eurika Kaiser, J. Nathan Kutz, Steven L. Brunton
Data-driven discovery of dynamics via machine learning is pushing the frontiers of modelling and control efforts, providing a tremendous opportunity to extend the reach of model predictive control (MPC). However, many leading methods in machine learning, such as neural networks (NN), require large volumes of training data, may not be interpretable, do not easily include known constraints a...... hiện toàn bộ