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We are concerned with the fuzzy stochastic differential equations driven by multidimensional Brownian motion viewed as a tool used to describe the behavior of dynamic systems operating in fuzzy ...

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...

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 dimensional latent ...

Calculus, including integration, differentiation, and differential equations are insufficient to model stochastic phenomena like noise disturbances of signals in engineering, uncertainty about future ...

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential ...

Many dynamic processes can be described mathematically with the aid of stochastic partial differential equations. Scientists have found a new method which helps to solve a certain class of such ...

Using conditional neural stochastic differential equations, the authors propose a means to improve the efficiency of generative adversarial networks and test ...

Contens: Introduction; A crash course in basic probability theory; Brownian motion and white noise; Stochastic integrals, It o s formula; Stochastic differential equations.

Patrick Kidger from Oxford will present his work at the intersection between differential equations and deep learning.