`2021-09-15 16:10:00``2021-09-15 17:40:00``Invitations to Mathematics - Dongbin Xiu``Title: Data Driven Modeling of Unknown Systems with Deep Neural Networks Speaker: Dongbin Xiu Abstract: We present a framework of predictive modeling of unknown system from measurement data. The method is designed to discover/approximate the unknown evolution operator behind the data. Deep neural network (DNN) is employed to construct such an approximation. Once an accurate DNN model for evolution operator is constructed, it serves as a predictive model for the unknown system and enables us to conduct system analysis. We demonstrate that residual network (ResNet) is particularly suitable for modeling autonomous dynamical systems. Extensions to other types of systems will be discussed, including non-autonomous systems, systems with uncertain parameters, and more importantly, systems with missing variables, as well as partial differential equations (PDEs).``EA 170``OSU ASC Drupal 8``ascwebservices@osu.edu``America/New_York``public`

`2021-09-15 16:10:00``2021-09-15 17:40:00``Invitations to Mathematics - Dongbin Xiu``Title: Data Driven Modeling of Unknown Systems with Deep Neural Networks Speaker: Dongbin Xiu Abstract: We present a framework of predictive modeling of unknown system from measurement data. The method is designed to discover/approximate the unknown evolution operator behind the data. Deep neural network (DNN) is employed to construct such an approximation. Once an accurate DNN model for evolution operator is constructed, it serves as a predictive model for the unknown system and enables us to conduct system analysis. We demonstrate that residual network (ResNet) is particularly suitable for modeling autonomous dynamical systems. Extensions to other types of systems will be discussed, including non-autonomous systems, systems with uncertain parameters, and more importantly, systems with missing variables, as well as partial differential equations (PDEs). ``EA 170``Department of Mathematics``math@osu.edu``America/New_York``public`**Title**: Data Driven Modeling of Unknown Systems with Deep Neural Networks

**Speaker**: Dongbin Xiu

**Abstract**:

We present a framework of predictive modeling of unknown system from measurement

data. The method is designed to discover/approximate the unknown evolution operator

behind the data. Deep neural network (DNN) is employed to construct such an

approximation. Once an accurate DNN model for evolution operator is constructed, it

serves as a predictive model for the unknown system and enables us to conduct system

analysis. We demonstrate that residual network (ResNet) is particularly suitable for

modeling autonomous dynamical systems. Extensions to other types of systems will be

discussed, including non-autonomous systems, systems with uncertain parameters, and

more importantly, systems with missing variables, as well as partial differential equations

(PDEs).