ResearchTrend.AI
On the training and generalization of deep operator networks

On the training and generalization of deep operator networks

Sanghyun Lee
Yeonjong Shin
ArXiv (abs)PDFHTML

Papers citing "On the training and generalization of deep operator networks"

13 / 13 papers shown
Title
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
Elham Kiyani
Venkatesh Ananchaperumal
Ahmad Peyvan
Mahendaran Uchimali
Gang Li
George Karniadakis
AI4CE
16
0
0
15 May 2025
Accelerating Multiscale Modeling with Hybrid Solvers: Coupling FEM and Neural Operators with Domain Decomposition
Accelerating Multiscale Modeling with Hybrid Solvers: Coupling FEM and Neural Operators with Domain Decomposition
Wei Wang
Maryam Hakimzadeh
Haihui Ruan
Somdatta Goswami
AI4CE
96
1
0
15 Apr 2025
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
Kamaljyoti Nath
Khemraj Shukla
Victor C. Tsai
Umair bin Waheed
Christian Huber
Omer Alpak
Chuen-Song Chen
Ligang Lu
Amik St-Cyr
55
0
0
14 Apr 2025
Physics-Informed Deep B-Spline Networks for Dynamical Systems
Physics-Informed Deep B-Spline Networks for Dynamical Systems
Zhuoyuan Wang
Raffaele Romagnoli
Jasmine Ratchford
Yorie Nakahira
PINNAI4CE
87
0
0
21 Mar 2025
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?
Neural Hamilton: Can A.I. Understand Hamiltonian Mechanics?
Tae-Geun Kim
Seong Chan Park
97
0
0
28 Oct 2024
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators
DeepOSets: Non-Autoregressive In-Context Learning of Supervised Learning Operators
Shao-Ting Chiu
Junyuan Hong
Ulisses Braga-Neto
BDL
71
1
0
11 Oct 2024
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study
Hao Liu
Zecheng Zhang
Wenjing Liao
Hayden Schaeffer
73
1
0
01 Oct 2024
Basis-to-Basis Operator Learning Using Function Encoders
Basis-to-Basis Operator Learning Using Function Encoders
Tyler Ingebrand
Adam J. Thorpe
Somdatta Goswami
Krishna Kumar
Ufuk Topcu
54
5
0
30 Sep 2024
Efficient Training of Deep Neural Operator Networks via Randomized Sampling
Efficient Training of Deep Neural Operator Networks via Randomized Sampling
Sharmila Karumuri
Lori Graham-Brady
Somdatta Goswami
82
2
0
20 Sep 2024
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity
Benjamin Shih
Ahmad Peyvan
Zhongqiang Zhang
George Karniadakis
AI4CE
94
12
0
29 May 2024
Ensemble and Mixture-of-Experts DeepONets For Operator Learning
Ensemble and Mixture-of-Experts DeepONets For Operator Learning
Ramansh Sharma
Varun Shankar
127
0
0
20 May 2024
RiemannONets: Interpretable Neural Operators for Riemann Problems
RiemannONets: Interpretable Neural Operators for Riemann Problems
Ahmad Peyvan
Vivek Oommen
Ameya Dilip Jagtap
George Karniadakis
AI4CE
100
26
0
16 Jan 2024
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Rethinking materials simulations: Blending direct numerical simulations with neural operators
Vivek Oommen
K. Shukla
Saaketh Desai
Rémi Dingreville
George Karniadakis
AI4CE
93
22
0
08 Dec 2023

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.