일정

Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks

기간 : 2024-05-24 ~ 2024-05-24
시간 : 15:00 ~ 17:00
개최 장소 : Math bldg. 404
개요
Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks
분야Field
날짜Date 2024-05-24 ~ 2024-05-24 시간Time 15:00 ~ 17:00
장소Place Math bldg. 404 초청자Host
연사Speaker Hyeontae Jo 소속Affiliation Korea University Sejong Campus
TOPIC Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks
소개 및 안내사항Content

Title : Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks

Speaker : Hyeontae Jo (Korea University Sejong Campus)

Abstract : A dynamical system $y’(t)=f(y)$ can be used to model the evolution of natural or engineered systems. Traditionally, the overall structure of the system $f$ is determined by researchers' insights (experience) or experimental observations (data). However, such insights might fail to capture the system's complexity and nonlinearities due to the limitations in human intuition and experimental precision. In response, data-driven scientific discovery methods have been developed by employing artificial neural networks (ANN). Specifically, ANN is trained to simultaneously fit the system $f$ and the data, called Physics-informed Neural Networks (PINN). In this presentation, we will study 1) the basic idea of the PINN 2) effectiveness of the PINN. Furthermore, we show how we can extend the concept of PINN to real-world dataset, thus understanding the structure of the system $f$.

학회명Field Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks
날짜Date 2024-05-24 ~ 2024-05-24 시간Time 15:00 ~ 17:00
장소Place Math bldg. 404 초청자Host
소개 및 안내사항Content

Title : Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks

Speaker : Hyeontae Jo (Korea University Sejong Campus)

Abstract : A dynamical system $y’(t)=f(y)$ can be used to model the evolution of natural or engineered systems. Traditionally, the overall structure of the system $f$ is determined by researchers' insights (experience) or experimental observations (data). However, such insights might fail to capture the system's complexity and nonlinearities due to the limitations in human intuition and experimental precision. In response, data-driven scientific discovery methods have been developed by employing artificial neural networks (ANN). Specifically, ANN is trained to simultaneously fit the system $f$ and the data, called Physics-informed Neural Networks (PINN). In this presentation, we will study 1) the basic idea of the PINN 2) effectiveness of the PINN. Furthermore, we show how we can extend the concept of PINN to real-world dataset, thus understanding the structure of the system $f$.

성명Field Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks
날짜Date 2024-05-24 ~ 2024-05-24 시간Time 15:00 ~ 17:00
소속Affiliation Korea University Sejong Campus 초청자Host
소개 및 안내사항Content

Title : Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks

Speaker : Hyeontae Jo (Korea University Sejong Campus)

Abstract : A dynamical system $y’(t)=f(y)$ can be used to model the evolution of natural or engineered systems. Traditionally, the overall structure of the system $f$ is determined by researchers' insights (experience) or experimental observations (data). However, such insights might fail to capture the system's complexity and nonlinearities due to the limitations in human intuition and experimental precision. In response, data-driven scientific discovery methods have been developed by employing artificial neural networks (ANN). Specifically, ANN is trained to simultaneously fit the system $f$ and the data, called Physics-informed Neural Networks (PINN). In this presentation, we will study 1) the basic idea of the PINN 2) effectiveness of the PINN. Furthermore, we show how we can extend the concept of PINN to real-world dataset, thus understanding the structure of the system $f$.

성명Field Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks
날짜Date 2024-05-24 ~ 2024-05-24 시간Time 15:00 ~ 17:00
호실Host 인원수Affiliation Hyeontae Jo
사용목적Affiliation 신청방식Host Korea University Sejong Campus
소개 및 안내사항Content

Title : Physics-informed neural networks: Fitting a mathematical model to real data using artificial neural networks

Speaker : Hyeontae Jo (Korea University Sejong Campus)

Abstract : A dynamical system $y’(t)=f(y)$ can be used to model the evolution of natural or engineered systems. Traditionally, the overall structure of the system $f$ is determined by researchers' insights (experience) or experimental observations (data). However, such insights might fail to capture the system's complexity and nonlinearities due to the limitations in human intuition and experimental precision. In response, data-driven scientific discovery methods have been developed by employing artificial neural networks (ANN). Specifically, ANN is trained to simultaneously fit the system $f$ and the data, called Physics-informed Neural Networks (PINN). In this presentation, we will study 1) the basic idea of the PINN 2) effectiveness of the PINN. Furthermore, we show how we can extend the concept of PINN to real-world dataset, thus understanding the structure of the system $f$.

Admin Admin · 2024-02-20 10:34 · 조회 18660
kartal escort maltepe escort