David Najera-Flores September 2022 - Revision history

Jadmin at 13:03, 13 September 2023

2023-09-13T13:03:00Z

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	[[Category:Seminar_Series]]		[[Category:Seminar_Series]]
	Structural_Dynamics		[[Category:Structural_Dynamics]]
	Simulation_Methods		[[Category:Simulation_Methods]]
	Nonlinear_Modal_Analysis		[[Category:Nonlinear_Modal_Analysis]]
	Nonlinear System Identification		[[Category:Nonlinear System Identification]]
	Data Driven Methods		[[Category:Data Driven Methods]]

Jadmin at 22:32, 12 September 2023

2023-09-12T22:32:58Z

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	[[Category:Seminar_Series]]		[[Category:Seminar_Series]]
			Structural_Dynamics
			Simulation_Methods
			Nonlinear_Modal_Analysis
			Nonlinear System Identification
			Data Driven Methods

Jadmin at 11:58, 18 April 2023

2023-04-18T11:58:48Z

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	{{DISPLAYTITLE:A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics}}		{{DISPLAYTITLE:A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics by David Najera-Flores, September 2022}}
	==A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics==		==A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics==

Jadmin at 15:55, 20 September 2022

2022-09-20T15:55:58Z

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Jadmin: /* A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics */

2022-09-09T17:35:50Z

A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics

← Older revision		Revision as of 13:35, 9 September 2022
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	David Najera-Flores		David Najera-Flores

	ATA Engineering		ATA Engineering

	University of California San Diego		University of California San Diego

Jadmin at 17:35, 9 September 2022

2022-09-09T17:35:17Z

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	{{DISPLAYTITLE:A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics ~~by David Najera-Flores, September 20, 2022~~}}		{{DISPLAYTITLE:A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics}}
	==A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics==		==A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics==

Jadmin at 17:34, 9 September 2022

2022-09-09T17:34:59Z

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	{{DISPLAYTITLE:Neural ~~Networks and Joints~~ by David Najera-Flores, September 2022}}		{{DISPLAYTITLE:A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics by David Najera-Flores, September 20, 2022}}
	==~~TBD~~==		==A Structure-Preserving Neural Differential Operator for Modeling Structural Dynamics==


			David Najera-Flores
			ATA Engineering
			University of California San Diego

	~~Sepember~~, 2022		September 20, 2022

	===Abstract:===		===Abstract:===

			Data-driven machine learning models are useful for modeling complex structures based on empirical observations, bypassing the need to generate a physical model where the physics is not well known or otherwise difficult to model. Among the disadvantages of purely data-driven approaches is that they tend to perform poorly in regions outside the original training domain, and that they lack physical interpretability. To mitigate these limitations, physical knowledge about the structure can be embedded in the neural network architecture. This talk provides an overview of a neural network framework based on Hamiltonian mechanics to enforce a physics-informed structure to the model. The Hamiltonian framework allows us to relate the energy of the system to the measured quantities through the Euler-Lagrange equations of motion. The approach incorporates a physics-constrained autoencoder to perform coordinate transformation between measured and generalized coordinates. This approach results in a physics-informed, structure-preserving model of the structure that can form the basis of a digital twin for many applications including structural health monitoring. This talk will present some of the applications and planned future work in this area.


	===Biography:===		===Biography:===

			David Najera-Flores is a Senior Project Engineer at ATA Engineering and a PhD student at the University of California San Diego under the supervision of Dr. Michael Todd. His research focuses on the application of data science methods to solve nonlinear dynamics problems and advanced material modeling. David has a Master’s degree in Computer Science from the University of Illinois Urbana-Champaign and a Master’s degree in Structural Engineering from the University of California San Diego.

	==Video Presentation ==		==Video Presentation ==

Jadmin: Created page with "{{DISPLAYTITLE:Neural Networks and Joints by David Najera-Flores, September 2022}} ==TBD== Sepember, 2022 ===Abstract:=== ===Biography:=== ==Video Presentation == {{#widget:Youtube |id = | height = 444 | width = 790 }} Category:Seminar_Series"

2022-07-18T13:24:30Z

Created page with "{{DISPLAYTITLE:Neural Networks and Joints by David Najera-Flores, September 2022}} ==TBD== Sepember, 2022 ===Abstract:=== ===Biography:=== ==Video Presentation == {{#widget:Youtube |id = | height = 444 | width = 790 }} Category:Seminar_Series"

New page

{{DISPLAYTITLE:Neural Networks and Joints by David Najera-Flores, September 2022}}
==TBD==

Sepember, 2022

===Abstract:===

===Biography:===

==Video Presentation ==
{{#widget:Youtube
|id =
| height = 444
| width = 790
}}

[[Category:Seminar_Series]]