ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.01302
  4. Cited By
Simulation free reliability analysis: A physics-informed deep learning
  based approach
v1v2v3 (latest)

Simulation free reliability analysis: A physics-informed deep learning based approach

4 May 2020
S. Chakraborty
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Simulation free reliability analysis: A physics-informed deep learning based approach"

12 / 12 papers shown
Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines
Physics-Informed Neural Network-based Reliability Analysis of Buried Pipelines
Pouya Taraghi
Yong Li
Samer Adeeb
AI4CE
71
0
0
04 Nov 2025
Harnessing physics-informed operators for high-dimensional reliability
  analysis problems
Harnessing physics-informed operators for high-dimensional reliability analysis problemsProbabilistic Engineering Mechanics (PEM), 2024
N Navaneeth
Tushar
Souvik Chakraborty
AI4CE
199
2
0
07 Sep 2024
Learning governing physics from output only measurements
Learning governing physics from output only measurements
Tapas Tripura
S. Chakraborty
142
1
0
11 Aug 2022
Gated Linear Model induced U-net for surrogate modeling and uncertainty
  quantification
Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification
Sai Krishna Mendu
S. Chakraborty
BDLAI4CE
195
2
0
08 Nov 2021
Physics-integrated hybrid framework for model form error identification
  in nonlinear dynamical systems
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systemsMechanical systems and signal processing (MSSP), 2021
Shailesh Garg
S. Chakraborty
B. Hazra
180
24
0
01 Sep 2021
GrADE: A graph based data-driven solver for time-dependent nonlinear
  partial differential equations
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations
Y. Kumar
S. Chakraborty
179
9
0
24 Aug 2021
Surrogate assisted active subspace and active subspace assisted
  surrogate -- A new paradigm for high dimensional structural reliability
  analysis
Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysisComputer Methods in Applied Mechanics and Engineering (CMAME), 2021
N. N.
S. Chakraborty
217
36
0
11 May 2021
Machine learning based digital twin for stochastic nonlinear
  multi-degree of freedom dynamical system
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical systemProbabilistic Engineering Mechanics (PEM), 2021
Shailesh Garg
Ankush Gogoi
S. Chakraborty
B. Hazra
AI4CE
173
21
0
29 Mar 2021
Uncertainty Quantification of Locally Nonlinear Dynamical Systems using
  Neural Networks
Uncertainty Quantification of Locally Nonlinear Dynamical Systems using Neural NetworksJournal of computing in civil engineering (J. Comput. Civ. Eng.), 2020
Subhayan De
153
10
0
11 Aug 2020
Transfer learning based multi-fidelity physics informed deep neural
  network
Transfer learning based multi-fidelity physics informed deep neural network
S. Chakraborty
PINNOODAI4CE
377
207
0
19 May 2020
Machine learning based digital twin for dynamical systems with multiple
  time-scales
Machine learning based digital twin for dynamical systems with multiple time-scales
S. Chakraborty
S. Adhikari
AI4CE
138
99
0
12 May 2020
The role of surrogate models in the development of digital twins of
  dynamic systems
The role of surrogate models in the development of digital twins of dynamic systemsApplied Mathematical Modelling (AMM), 2020
S. Chakraborty
S. Adhikari
R. Ganguli
SyDa
156
123
0
25 Jan 2020
1
Page 1 of 1