Zobrazeno 1 - 10
of 966
pro vyhledávání: '"N. Benjamin"'
Publikováno v:
Communications Physics, Vol 6, Iss 1, Pp 1-14 (2023)
Abstract Dynamical systems that evolve continuously over time are ubiquitous throughout science and engineering. Machine learning (ML) provides data-driven approaches to model and predict the dynamics of such systems. A core issue with this approach
Externí odkaz:
https://doaj.org/article/776f47981bd7428aab296c6fe960eb7f
Publikováno v:
Communications Physics, Vol 5, Iss 1, Pp 1-9 (2022)
Moiré patterns and ordered aperiodic geometries have received significant attention since their observation in twisted bilayer graphene and quasicrystalline alloys. Here, the authors theoretically demonstrate that the same patterns can govern locali
Externí odkaz:
https://doaj.org/article/dc48511df4754556834baf5cfe0fc701
Autor:
Lim, Soon Hoe, Wang, Yijin, Yu, Annan, Hart, Emma, Mahoney, Michael W., Li, Xiaoye S., Erichson, N. Benjamin
Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impact of the specific choice of probability path model on forecasting per
Externí odkaz:
http://arxiv.org/abs/2410.03229
State space models (SSMs) leverage linear, time-invariant (LTI) systems to effectively learn sequences with long-range dependencies. By analyzing the transfer functions of LTI systems, we find that SSMs exhibit an implicit bias toward capturing low-f
Externí odkaz:
http://arxiv.org/abs/2410.02035
Autor:
Ren, Pu, Nakata, Rie, Lacour, Maxime, Naiman, Ilan, Nakata, Nori, Song, Jialin, Bi, Zhengfa, Malik, Osman Asif, Morozov, Dmitriy, Azencot, Omri, Erichson, N. Benjamin, Mahoney, Michael W.
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake l
Externí odkaz:
http://arxiv.org/abs/2407.15089
Publikováno v:
Journal of Statistical Software, Vol 89, Iss 1, Pp 1-48 (2019)
Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and dat
Externí odkaz:
https://doaj.org/article/1e024a87da0945ef89448a9a2f06fb4b
Autor:
Lyu, Dongwei, Nakata, Rie, Ren, Pu, Mahoney, Michael W., Pitarka, Arben, Nakata, Nori, Erichson, N. Benjamin
Large earthquakes can be destructive and quickly wreak havoc on a landscape. To mitigate immediate threats, early warning systems have been developed to alert residents, emergency responders, and critical infrastructure operators seconds to a minute
Externí odkaz:
http://arxiv.org/abs/2405.20516
State-space models (SSMs) that utilize linear, time-invariant (LTI) systems are known for their effectiveness in learning long sequences. To achieve state-of-the-art performance, an SSM often needs a specifically designed initialization, and the trai
Externí odkaz:
http://arxiv.org/abs/2405.13975
Generating realistic time series data is important for many engineering and scientific applications. Existing work tackles this problem using generative adversarial networks (GANs). However, GANs are unstable during training, and they can suffer from
Externí odkaz:
http://arxiv.org/abs/2310.02619
State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks. An example is the structured state-space sequence (S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO initialization framework
Externí odkaz:
http://arxiv.org/abs/2310.01698