강연 / 세미나
MINDS SeminarㅣData Driven Modeling of Unknown Systems with Deep Neural Networks
| 분야Field | |||
|---|---|---|---|
| 날짜Date | 2023-05-10 ~ 2023-05-10 | 시간Time | 16:00 ~ 17:00 |
| 장소Place | Math Bldg 404&Online streaming (Zoom) | 초청자Host | 최민석 |
| 연사Speaker | Dongbin Xiu | 소속Affiliation | Ohio state university |
| TOPIC | MINDS SeminarㅣData Driven Modeling of Unknown Systems with Deep Neural Networks | ||
| 소개 및 안내사항Content | We present a framework of predictive modeling of unknown system from measurement data. The method is designed to discover/approximate the unknown evolution operator, i.e., flow map, behind the data. Deep neural network (DNN) is employed to construct such an approximation. Once an accurate DNN model for the evolution operator is constructed, it serves as a predictive model for the unknown system and enables us to conduct system analysis. We demonstrate that flow map learning (FML) approach is applicable for modeling a wide class of problems, including dynamical systems, systems with missing variables and hidden parameters, as well as partial differential equations (PDEs). https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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| 학회명Field | MINDS SeminarㅣData Driven Modeling of Unknown Systems with Deep Neural Networks | ||
|---|---|---|---|
| 날짜Date | 2023-05-10 ~ 2023-05-10 | 시간Time | 16:00 ~ 17:00 |
| 장소Place | Math Bldg 404&Online streaming (Zoom) | 초청자Host | 최민석 |
| 소개 및 안내사항Content | We present a framework of predictive modeling of unknown system from measurement data. The method is designed to discover/approximate the unknown evolution operator, i.e., flow map, behind the data. Deep neural network (DNN) is employed to construct such an approximation. Once an accurate DNN model for the evolution operator is constructed, it serves as a predictive model for the unknown system and enables us to conduct system analysis. We demonstrate that flow map learning (FML) approach is applicable for modeling a wide class of problems, including dynamical systems, systems with missing variables and hidden parameters, as well as partial differential equations (PDEs). https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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| 성명Field | MINDS SeminarㅣData Driven Modeling of Unknown Systems with Deep Neural Networks | ||
|---|---|---|---|
| 날짜Date | 2023-05-10 ~ 2023-05-10 | 시간Time | 16:00 ~ 17:00 |
| 소속Affiliation | Ohio state university | 초청자Host | 최민석 |
| 소개 및 안내사항Content | We present a framework of predictive modeling of unknown system from measurement data. The method is designed to discover/approximate the unknown evolution operator, i.e., flow map, behind the data. Deep neural network (DNN) is employed to construct such an approximation. Once an accurate DNN model for the evolution operator is constructed, it serves as a predictive model for the unknown system and enables us to conduct system analysis. We demonstrate that flow map learning (FML) approach is applicable for modeling a wide class of problems, including dynamical systems, systems with missing variables and hidden parameters, as well as partial differential equations (PDEs). https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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| 성명Field | MINDS SeminarㅣData Driven Modeling of Unknown Systems with Deep Neural Networks | ||
|---|---|---|---|
| 날짜Date | 2023-05-10 ~ 2023-05-10 | 시간Time | 16:00 ~ 17:00 |
| 호실Host | 인원수Affiliation | Dongbin Xiu | |
| 사용목적Affiliation | 최민석 | 신청방식Host | Ohio state university |
| 소개 및 안내사항Content | We present a framework of predictive modeling of unknown system from measurement data. The method is designed to discover/approximate the unknown evolution operator, i.e., flow map, behind the data. Deep neural network (DNN) is employed to construct such an approximation. Once an accurate DNN model for the evolution operator is constructed, it serves as a predictive model for the unknown system and enables us to conduct system analysis. We demonstrate that flow map learning (FML) approach is applicable for modeling a wide class of problems, including dynamical systems, systems with missing variables and hidden parameters, as well as partial differential equations (PDEs). https://us06web.zoom.us/j/6888961076?pwd=ejYxN05jNmhUa25PU2JzSUJvQ1haQT09 ID : 688 896 1076 / PW : 54321 |
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Admin
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2023-04-27 09:34 ·
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