Physics-informed neural networks for inverse problems in supersonic flows
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Physics-informed neural networks for inverse problems in supersonic flows
Authors
Keywords
-
Journal
JOURNAL OF COMPUTATIONAL PHYSICS
Volume 466, Issue -, Pages 111402
Publisher
Elsevier BV
Online
2022-06-23
DOI
10.1016/j.jcp.2022.111402
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations
- (2022) Ameya D. Jagtap et al. OCEAN ENGINEERING
- Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks
- (2021) Shengze Cai et al. JOURNAL OF FLUID MECHANICS
- A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
- (2021) Ehsan Haghighat et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A data-driven physics-informed finite-volume scheme for nonclassical undercompressive shocks
- (2021) Deniz A. Bezgin et al. JOURNAL OF COMPUTATIONAL PHYSICS
- A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems
- (2021) Khemraj Shukla et al. IEEE SIGNAL PROCESSING MAGAZINE
- Thermodynamically consistent physics-informed neural networks for hyperbolic systems
- (2021) Ravi G. Patel et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Parallel physics-informed neural networks via domain decomposition
- (2021) Khemraj Shukla et al. JOURNAL OF COMPUTATIONAL PHYSICS
- DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
- (2021) Zhiping Mao et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
- (2020) Maziar Raissi et al. SCIENCE
- Inverting shock-wave temperatures via artificial neural networks
- (2020) Zhiyu He et al. JOURNAL OF APPLIED PHYSICS
- Constraint-aware neural networks for Riemann problems
- (2020) Jim Magiera et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Physics-informed neural networks for inverse problems in nano-optics and metamaterials
- (2020) Yuyao Chen et al. OPTICS EXPRESS
- Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
- (2020) Ameya D. Jagtap et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- (2020) Ameya D. Jagtap et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- hp-VPINNs: Variational physics-informed neural networks with domain decomposition
- (2020) Ehsan Kharazmi et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Adversarial uncertainty quantification in physics-informed neural networks
- (2019) Yibo Yang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- (2019) Ameya D. Jagtap et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Physics-informed neural networks for high-speed flows
- (2019) Zhiping Mao et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started