ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2109.04304
  4. Cited By
DAE-PINN: A Physics-Informed Neural Network Model for Simulating
  Differential-Algebraic Equations with Application to Power Networks

DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks

9 September 2021
Christian Moya
Guang Lin
    AI4CE
    PINN
ArXivPDFHTML

Papers citing "DAE-PINN: A Physics-Informed Neural Network Model for Simulating Differential-Algebraic Equations with Application to Power Networks"

9 / 9 papers shown
Title
DAE-KAN: A Kolmogorov-Arnold Network Model for High-Index Differential-Algebraic Equations
DAE-KAN: A Kolmogorov-Arnold Network Model for High-Index Differential-Algebraic Equations
Kai Luo
Juan Tang
Mingchao Cai
Xiaoqing Zeng
Manqi Xie
Ming Yan
17
0
0
22 Apr 2025
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks
Cyrus Neary
Nathan Tsao
Ufuk Topcu
72
1
0
15 Dec 2024
Quantum Neural Networks for Solving Power System Transient Simulation
  Problem
Quantum Neural Networks for Solving Power System Transient Simulation Problem
Mohammadreza Soltaninia
Junpeng Zhan
18
0
0
19 May 2024
Physical Information Neural Networks for Solving High-index
  Differential-algebraic Equation Systems Based on Radau Methods
Physical Information Neural Networks for Solving High-index Differential-algebraic Equation Systems Based on Radau Methods
Jiasheng Chen
Juan Tang
Ming Yan
Shuai Lai
Kun Liang
Jianguang Lu
Wenqiang Yang
AI4CE
14
0
0
19 Oct 2023
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy,
  Computational Cost, and Flexibility
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility
Jochen Stiasny
Spyros Chatzivasileiadis
PINN
AI4CE
11
9
0
15 Mar 2023
On Approximating the Dynamic Response of Synchronous Generators via
  Operator Learning: A Step Towards Building Deep Operator-based Power Grid
  Simulators
On Approximating the Dynamic Response of Synchronous Generators via Operator Learning: A Step Towards Building Deep Operator-based Power Grid Simulators
Christian Moya
Guang Lin
Tianqiao Zhao
Meng Yue
27
8
0
29 Jan 2023
glassoformer: a query-sparse transformer for post-fault power grid
  voltage prediction
glassoformer: a query-sparse transformer for post-fault power grid voltage prediction
Yunling Zheng
Carson Hu
Guang Lin
Meng Yue
Bao Wang
Jack Xin
60
2
0
22 Jan 2022
Physics-informed neural networks with hard constraints for inverse
  design
Physics-informed neural networks with hard constraints for inverse design
Lu Lu
R. Pestourie
Wenjie Yao
Zhicheng Wang
F. Verdugo
Steven G. Johnson
PINN
39
489
0
09 Feb 2021
Informer: Beyond Efficient Transformer for Long Sequence Time-Series
  Forecasting
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou
Shanghang Zhang
J. Peng
Shuai Zhang
Jianxin Li
Hui Xiong
Wan Zhang
AI4TS
161
3,799
0
14 Dec 2020
1