176 related articles for article (PubMed ID: 37583614)
1. Error estimates and physics informed augmentation of neural networks for thermally coupled incompressible Navier Stokes equations.
Goraya S; Sobh N; Masud A
Comput Mech; 2023 Aug; 72(2):267-289. PubMed ID: 37583614
[TBL] [Abstract][Full Text] [Related]
2. Dynamic Weight Strategy of Physics-Informed Neural Networks for the 2D Navier-Stokes Equations.
Li S; Feng X
Entropy (Basel); 2022 Sep; 24(9):. PubMed ID: 36141140
[TBL] [Abstract][Full Text] [Related]
3. Rigorous a Posteriori Error Bounds for PDE-Defined PINNs.
Hillebrecht B; Unger B
IEEE Trans Neural Netw Learn Syst; 2023 Dec; PP():. PubMed ID: 38039171
[TBL] [Abstract][Full Text] [Related]
4. Non-invasive Inference of Thrombus Material Properties with Physics-Informed Neural Networks.
Yin M; Zheng X; Humphrey JD; Em Karniadakis G
Comput Methods Appl Mech Eng; 2021 Mar; 375():. PubMed ID: 33414569
[TBL] [Abstract][Full Text] [Related]
5. Gradient Statistics-Based Multi-Objective Optimization in Physics-Informed Neural Networks.
Vemuri SK; Denzler J
Sensors (Basel); 2023 Oct; 23(21):. PubMed ID: 37960365
[TBL] [Abstract][Full Text] [Related]
6. Tackling the curse of dimensionality with physics-informed neural networks.
Hu Z; Shukla K; Karniadakis GE; Kawaguchi K
Neural Netw; 2024 Aug; 176():106369. PubMed ID: 38754287
[TBL] [Abstract][Full Text] [Related]
7. An improved data-free surrogate model for solving partial differential equations using deep neural networks.
Chen X; Chen R; Wan Q; Xu R; Liu J
Sci Rep; 2021 Sep; 11(1):19507. PubMed ID: 34593943
[TBL] [Abstract][Full Text] [Related]
8. Affine transformations accelerate the training of physics-informed neural networks of a one-dimensional consolidation problem.
Mandl L; Mielke A; Seyedpour SM; Ricken T
Sci Rep; 2023 Sep; 13(1):15566. PubMed ID: 37730743
[TBL] [Abstract][Full Text] [Related]
9. Physics-informed attention-based neural network for hyperbolic partial differential equations: application to the Buckley-Leverett problem.
Rodriguez-Torrado R; Ruiz P; Cueto-Felgueroso L; Green MC; Friesen T; Matringe S; Togelius J
Sci Rep; 2022 May; 12(1):7557. PubMed ID: 35534639
[TBL] [Abstract][Full Text] [Related]
10. A Combination of Deep Neural Networks and Physics to Solve the Inverse Problem of Burger's Equation.
Alkhadhr S; Almekkawy M
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():4465-4468. PubMed ID: 34892210
[TBL] [Abstract][Full Text] [Related]
11. Physics-informed kernel function neural networks for solving partial differential equations.
Fu Z; Xu W; Liu S
Neural Netw; 2024 Apr; 172():106098. PubMed ID: 38199153
[TBL] [Abstract][Full Text] [Related]
12. nn-PINNs: Non-Newtonian physics-informed neural networks for complex fluid modeling.
Mahmoudabadbozchelou M; Karniadakis GE; Jamali S
Soft Matter; 2021 Dec; 18(1):172-185. PubMed ID: 34859251
[TBL] [Abstract][Full Text] [Related]
13. Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system.
Kumar AK; Jain S; Jain S; Ritam M; Xia Y; Chandra R
Comput Methods Programs Biomed; 2023 Apr; 231():107421. PubMed ID: 36805280
[TBL] [Abstract][Full Text] [Related]
14. Use of high-speed angiography HSA-derived boundary conditions and Physics Informed Neural Networks (PINNs) for comprehensive estimation of neurovascular hemodynamics.
Williams KA; Shields A; Bhurwani MMS; Nagesh SVS; Bednarek DR; Rudin S; Ionita CN
Proc SPIE Int Soc Opt Eng; 2023 Feb; 12463():. PubMed ID: 37424833
[TBL] [Abstract][Full Text] [Related]
15. Deep learning-accelerated computational framework based on Physics Informed Neural Network for the solution of linear elasticity.
Roy AM; Bose R; Sundararaghavan V; Arróyave R
Neural Netw; 2023 May; 162():472-489. PubMed ID: 36966712
[TBL] [Abstract][Full Text] [Related]
16. A Second-Order Network Structure Based on Gradient-Enhanced Physics-Informed Neural Networks for Solving Parabolic Partial Differential Equations.
Sun K; Feng X
Entropy (Basel); 2023 Apr; 25(4):. PubMed ID: 37190465
[TBL] [Abstract][Full Text] [Related]
17. Material Data Identification in an Induction Hardening Test Rig with Physics-Informed Neural Networks.
Asadzadeh MZ; Roppert K; Raninger P
Materials (Basel); 2023 Jul; 16(14):. PubMed ID: 37512288
[TBL] [Abstract][Full Text] [Related]
18. Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks.
Gnanasambandam R; Shen B; Chung J; Yue X; Kong Z
IEEE Trans Pattern Anal Mach Intell; 2023 Dec; 45(12):15588-15603. PubMed ID: 37610913
[TBL] [Abstract][Full Text] [Related]
19. Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks.
Karnakov P; Litvinov S; Koumoutsakos P
PNAS Nexus; 2024 Jan; 3(1):pgae005. PubMed ID: 38250513
[TBL] [Abstract][Full Text] [Related]
20. The Old and the New: Can Physics-Informed Deep-Learning Replace Traditional Linear Solvers?
Markidis S
Front Big Data; 2021; 4():669097. PubMed ID: 34870188
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
