Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...
We consider a model for Darwinian evolution in an asexual population with a large but nonconstant populations size characterized by a natural birth rate, a logistic death rate modeling competition and ...
The Annals of Statistics, Vol. 48, No. 6 (December 2020), pp. 3336-3365 (30 pages) We consider N independent stochastic processes (Xi (t), t ∈ [0, T]), i = 1, . . . , N, defined by a one-dimensional ...
Abstract: Ultra-short-term probabilistic wind power forecasting provides paramount uncertainty information for power system real-time operation. However, the stochastic dynamics of wind power ...
To mathematicians, equations are art. Just as many are moved by a painting or piece of music, to those who appreciate and understand math, expressions of numbers, variables, operations and relations ...
A new algorithm developed by Naoki Masuda, with co-athors Kazuyuki Aihara and Neil G. MacLaren, can identify the most predictive data points that a tipping point is near. Published in Nature ...
Abstract: We present a method for learning latent stochastic differential equations (SDEs) from high dimensional time series data. Given a high-dimensional time series generated from a lower ...
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