TY  - JOUR
A1  - Licht, Alexis
A1  - van Cappelle, M.
A1  - Abels, Hemmo A.
A1  - Ladant, Jean-Baptiste
A1  - Trabucho-Alexandre, J.
A1  - France-Lanord, C.
A1  - Donnadieu, Yannick
A1  - Vandenberghe, J.
A1  - Rigaudier, T.
A1  - Lecuyer, C.
A1  - Terry, D.
A1  - Adriaens, R.
A1  - Boura, A.
A1  - Guo, Z.
A1  - Soe, Aung Naing
A1  - Quade, J.
A1  - Dupont-Nivet, Guillaume
A1  - Jaeger, J. -J.
T1  - Asian monsoons in a late Eocene greenhouse world
JF  - Nature : the international weekly journal of science
N2  - The strong present-day Asian monsoons are thought to have originated between 25 and 22 million years (Myr) ago, driven by Tibetan-Himalayan uplift. However, the existence of older Asian monsoons and their response to enhanced greenhouse conditions such as those in the Eocene period (55-34Myrago) are unknown because of the paucity of well-dated records. Here we show late Eocene climate records revealing marked monsoon-like patterns in rainfall and wind south and north of the Tibetan-Himalayan orogen. This is indicated by low oxygen isotope values with strong seasonality in gastropod shells and mammal teeth from Myanmar, and by aeolian dust deposition in northwest China. Our climate simulations support modern-like Eocene monsoonal rainfall and show that a reinforced hydrological cycle responding to enhanced greenhouse conditions counterbalanced the negative effect of lower Tibetan relief on precipitation. These strong monsoons later weakened with the global shift to icehouse conditions 34 Myr ago.
Y1  - 2014
U6  - https://doi.org/10.1038/nature13704
SN  - 0028-0836
SN  - 1476-4687
VL  - 513
IS  - 7519
SP  - 501
EP  - +
PB  - Nature Publ. Group
CY  - London
ER  - 
TY  - JOUR
A1  - Quade, Markus
A1  - Abel, Markus
A1  - Kutz, J. Nathan
A1  - Brunton, Steven L.
T1  - Sparse identification of nonlinear dynamics for rapid model recovery
JF  - Chaos : an interdisciplinary journal of nonlinear science
N2  - Big data have become a critically enabling component of emerging mathematical methods aimed at the automated discovery of dynamical systems, where first principles modeling may be intractable. However, in many engineering systems, abrupt changes must be rapidly characterized based on limited, incomplete, and noisy data. Many leading automated learning techniques rely on unrealistically large data sets, and it is unclear how to leverage prior knowledge effectively to re-identify a model after an abrupt change. In this work, we propose a conceptual framework to recover parsimonious models of a system in response to abrupt changes in the low-data limit. First, the abrupt change is detected by comparing the estimated Lyapunov time of the data with the model prediction. Next, we apply the sparse identification of nonlinear dynamics (SINDy) regression to update a previously identified model with the fewest changes, either by addition, deletion, or modification of existing model terms. We demonstrate this sparse model recovery on several examples for abrupt system change detection in periodic and chaotic dynamical systems. Our examples show that sparse updates to a previously identified model perform better with less data, have lower runtime complexity, and are less sensitive to noise than identifying an entirely new model. The proposed abrupt-SINDy architecture provides a new paradigm for the rapid and efficient recovery of a system model after abrupt changes.
Y1  - 2018
U6  - https://doi.org/10.1063/1.5027470
SN  - 1054-1500
SN  - 1089-7682
VL  - 28
IS  - 6
PB  - American Institute of Physics
CY  - Melville
ER  -