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. 2505.14252
  4. Cited By
Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks

Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks

20 May 2025
Mouad Elaarabi
Domenico Borzacchiello
Philippe Le Bot
Nathan Lauzeral
Sebastien Comas-Cardona
    PINNAI4CE
ArXiv (abs)PDFHTML

Papers citing "Hybrid Adaptive Modeling in Process Monitoring: Leveraging Sequence Encoders and Physics-Informed Neural Networks"

12 / 12 papers shown
Title
Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks
Adaptive parameters identification for nonlinear dynamics using deep permutation invariant networks
Mouad Elaarabi
Domenico Borzacchiello
Yves Le Guennec
Philippe Le Bot
Sebastien Comas-Cardona
156
1
0
20 Jan 2025
Forecast Evaluation for Data Scientists: Common Pitfalls and Best
  Practices
Forecast Evaluation for Data Scientists: Common Pitfalls and Best Practices
Hansika Hewamalage
Klaus Ackermann
Christoph Bergmeir
AI4TS
126
94
0
21 Mar 2022
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Michael Penwarden
Shandian Zhe
A. Narayan
Robert M. Kirby
77
45
0
25 Jun 2021
Any equation is a forest: Symbolic genetic algorithm for discovering
  open-form partial differential equations (SGA-PDE)
Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)
Yuntian Chen
Yingtao Luo
Qiang Liu
Hao Xu
Dongxiao Zhang
AI4CE
76
57
0
09 Jun 2021
Fourier Features Let Networks Learn High Frequency Functions in Low
  Dimensional Domains
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
Matthew Tancik
Pratul P. Srinivasan
B. Mildenhall
Sara Fridovich-Keil
N. Raghavan
Utkarsh Singhal
R. Ramamoorthi
Jonathan T. Barron
Ren Ng
126
2,440
0
18 Jun 2020
Physics-informed learning of governing equations from scarce data
Physics-informed learning of governing equations from scarce data
Zhao Chen
Yang Liu
Hao Sun
PINNAI4CE
74
396
0
05 May 2020
DeepXDE: A deep learning library for solving differential equations
DeepXDE: A deep learning library for solving differential equations
Lu Lu
Xuhui Meng
Zhiping Mao
George Karniadakis
PINNAI4CE
99
1,540
0
10 Jul 2019
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep
  Network
PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Zichao Long
Yiping Lu
Bin Dong
AI4CE
76
553
0
30 Nov 2018
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
A Unified Framework for Sparse Relaxed Regularized Regression: SR3
P. Zheng
T. Askham
Steven L. Brunton
J. Nathan Kutz
Aleksandr Aravkin
43
139
0
14 Jul 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Neural Tangent Kernel: Convergence and Generalization in Neural Networks
Arthur Jacot
Franck Gabriel
Clément Hongler
273
3,219
0
20 Jun 2018
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
786
132,363
0
12 Jun 2017
Deep Sets
Deep Sets
Manzil Zaheer
Satwik Kottur
Siamak Ravanbakhsh
Barnabás Póczós
Ruslan Salakhutdinov
Alex Smola
421
2,478
0
10 Mar 2017
1