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Neural Dynamics for Science : = The Symbiosis of Deep Graph Learning and Differential Equations.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Neural Dynamics for Science :/
Reminder of title:	The Symbiosis of Deep Graph Learning and Differential Equations.
Author:	Huang, Zijie.
Description:	1 online resource (159 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798382837741

Neural Dynamics for Science : = The Symbiosis of Deep Graph Learning and Differential Equations.
Huang, Zijie.

Neural Dynamics for Science :The Symbiosis of Deep Graph Learning and Differential Equations. - 1 online resource (159 pages)

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2024.

Includes bibliographical references

Many scientific problems require a deep understanding of internal structures and complex dynamics, spanning physical interactions within molecules, brain networks, and beyond. These problems can be formulated as modeling interacting dynamical systems using graphs, which represent entities as nodes and their relationship as edges. Traditionally, the dynamics of interacting systems are described by ordinary differential equations (ODEs), offering continuous and interpretable solutions but requiring significant domain expertise. Recent data-driven approaches such as Graph Neural Networks (GNNs) learn system dynamics from observational data, which however, struggle with long-term predictions and irregular observations due to their discrete dynamics.My research aims to develop novel frameworks that bridge these two worlds, i.e. combining the learning power of neural networks (NNs) with the symbolic knowledge encoded in ODEs. In contrast with discrete models, such methods provide a principled approach to model continuous dynamical systems, from synthetic simulations to real-world scenarios like brain network analysis and COVID-19 prediction. Building upon this, I have further strengthened its power in three key areas: 1.) integrating data-driven inductive biases like energy conservation law; 2.) enhancing generalization ability; 3.) enabling causal decision-making. By merging deep graph learning with differential equations, I believe my research will pave the way for breakthroughs in symbolic deep learning for scientific discovery.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382837741Subjects--Topical Terms:

569006
Computer engineering.
Subjects--Index Terms:

Graph Neural NetworksIndex Terms--Genre/Form:

554714
Electronic books.

Neural Dynamics for Science : = The Symbiosis of Deep Graph Learning and Differential Equations.
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500 $a Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
500 $a Advisor: Sun, Yizhou;Wang, Wei.
502 $a Thesis (Ph.D.)--University of California, Los Angeles, 2024.
504 $a Includes bibliographical references
520 $a Many scientific problems require a deep understanding of internal structures and complex dynamics, spanning physical interactions within molecules, brain networks, and beyond. These problems can be formulated as modeling interacting dynamical systems using graphs, which represent entities as nodes and their relationship as edges. Traditionally, the dynamics of interacting systems are described by ordinary differential equations (ODEs), offering continuous and interpretable solutions but requiring significant domain expertise. Recent data-driven approaches such as Graph Neural Networks (GNNs) learn system dynamics from observational data, which however, struggle with long-term predictions and irregular observations due to their discrete dynamics.My research aims to develop novel frameworks that bridge these two worlds, i.e. combining the learning power of neural networks (NNs) with the symbolic knowledge encoded in ODEs. In contrast with discrete models, such methods provide a principled approach to model continuous dynamical systems, from synthetic simulations to real-world scenarios like brain network analysis and COVID-19 prediction. Building upon this, I have further strengthened its power in three key areas: 1.) integrating data-driven inductive biases like energy conservation law; 2.) enhancing generalization ability; 3.) enabling causal decision-making. By merging deep graph learning with differential equations, I believe my research will pave the way for breakthroughs in symbolic deep learning for scientific discovery.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 573171
653 $a Graph Neural Networks
653 $a Ordinary differential equations
653 $a COVID-19
653 $a Deep graph learning
653 $a Brain networks
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