Physics-informed neural networks for inverse problems in supersonic flows

23 February 2022

Nikolaus Adams

Papers citing "Physics-informed neural networks for inverse problems in supersonic flows"

48 / 48 papers shown

Title
A general physics-constrained method for the modelling of equation's closure terms with sparse data Tian Chen Shengping Liu Li Liu Heng Yong PINN AI4CE 51 0 0 30 Apr 2025
Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometries Ali Kashefi T. Mukerji 3DPC PINN 52 0 0 08 Apr 2025
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study J. Abbasi Ameya D. Jagtap Ben Moseley Aksel Hiorth P. Andersen PINN AI4CE 52 1 0 14 Mar 2025
PIED: Physics-Informed Experimental Design for Inverse Problems Apivich Hemachandra Gregory Kang Ruey Lau Szu Hui Ng Bryan Kian Hsiang Low PINN 48 0 0 10 Mar 2025
Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Girnar Goyal Philipp Holl Sweta Agrawal Nils Thuerey AI4CE 48 0 0 27 Jan 2025
A Digital twin for Diesel Engines: Operator-infused PINNs with Transfer Learning for Engine Health Monitoring Kamaljyoti Nath Varun V. Kumar Daniel J. Smith George Karniadakis 81 0 0 16 Dec 2024
Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis Hyunwoo Lee Hayoung Choi Hyunju Kim 39 1 0 03 Oct 2024
Adapting Physics-Informed Neural Networks for Bifurcation Detection in Ecological Migration Models Lujie Yin Xing Lv PINN 23 0 0 01 Sep 2024
Neural networks for bifurcation and linear stability analysis of steady states in partial differential equations M. L. Shahab Hadi Susanto 27 2 0 29 Jul 2024
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be? Jorge F. Urbán P. Stefanou José A. Pons PINN 45 6 0 07 May 2024
Taper-based scattering formulation of the Helmholtz equation to improve the training process of Physics-Informed Neural Networks W. Dörfler Mehdi Elasmi Tim Laufer 33 0 0 15 Apr 2024
PINNACLE: PINN Adaptive ColLocation and Experimental points selection Gregory Kang Ruey Lau Apivich Hemachandra See-Kiong Ng K. H. Low 3DPC 37 18 0 11 Apr 2024
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations Hang Hu Sidi Wu Guoxiong Cai Na Liu 20 1 0 13 Mar 2024
JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework Artur P. Toshev Harish D. Ramachandran Jonas A. Erbesdobler Gianluca Galletti Johannes Brandstetter Nikolaus A. Adams 46 7 0 07 Mar 2024
JAX-Fluids 2.0: Towards HPC for Differentiable CFD of Compressible Two-phase Flows Deniz A. Bezgin Aaron B. Buhendwa Nikolaus A. Adams 20 9 0 07 Feb 2024
Preconditioning for Physics-Informed Neural Networks Songming Liu Chang Su J. Yao Zhongkai Hao Hang Su Youjia Wu Jun Zhu AI4CE PINN 44 5 0 01 Feb 2024
Physically Informed Synchronic-adaptive Learning for Industrial Systems Modeling in Heterogeneous Media with Unavailable Time-varying Interface Aina Wang Pan Qin Xi-Ming Sun PINN AI4CE 26 0 0 26 Jan 2024
RiemannONets: Interpretable Neural Operators for Riemann Problems Ahmad Peyvan Vivek Oommen Ameya Dilip Jagtap George Karniadakis AI4CE 44 22 0 16 Jan 2024
Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation Farhad Pourkamali-Anaraki Jamal F. Husseini E. Pineda B. Bednarcyk Scott E. Stapleton AI4CE 44 2 0 04 Jan 2024
Physics-informed neural network for modeling dynamic linear elasticity Vijay Kag Venkatesh Gopinath PINN 9 1 0 23 Dec 2023
Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans Ray Zirui Zhang Ivan Ezhov Michal Balcerak Andy Zhu Benedikt Wiestler Bjoern H. Menze John S. Lowengrub AI4CE 52 6 0 28 Nov 2023
Physics-informed neural network for acoustic resonance analysis in a one-dimensional acoustic tube Kazuya Yokota Takahiko Kurahashi Masajiro Abe 18 5 0 18 Oct 2023
Learning characteristic parameters and dynamics of centrifugal pumps under multi-phase flow using physics-informed neural networks Felipe de Castro Teixeira Carvalho Kamaljyoti Nath A. Serpa George Karniadakis 24 4 0 04 Oct 2023
Data-driven localized waves and parameter discovery in the massive Thirring model via extended physics-informed neural networks with interface zones Christian Berger Sadok Ben Toumia Zijian Zhou Zhenya Yan PINN 30 8 0 29 Sep 2023
Deep smoothness WENO scheme for two-dimensional hyperbolic conservation laws: A deep learning approach for learning smoothness indicators Tatiana Kossaczká Ameya Dilip Jagtap Matthias Ehrhardt 25 1 0 18 Sep 2023
Approximating High-Dimensional Minimal Surfaces with Physics-Informed Neural Networks Steven Zhou Xiaojing Ye PINN 13 0 0 05 Sep 2023
A Domain-adaptive Physics-informed Neural Network for Inverse Problems of Maxwell's Equations in Heterogeneous Media Shiyuan Piao Hong Gu Aina Wang Pan Qin PINN 21 2 0 12 Aug 2023
Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization Niranjan Sitapure J. Kwon 35 33 0 25 Jul 2023
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations R. Gao Yufeng Wang Min Yang Chuanjun Chen GAN 34 2 0 21 Jul 2023
A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part I Rajat Arora PINN AI4CE 27 1 0 09 Jul 2023
Parameter Identification for Partial Differential Equations with Spatiotemporal Varying Coefficients Guangtao Zhang Yiting Duan Guanyu Pan Qijing Chen Huiyu Yang Zhikun Zhang 19 0 0 30 Jun 2023
CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers Niranjan Sitapure J. Kwon 21 51 0 31 May 2023
Conservative Physics-Informed Neural Networks for Non-Conservative Hyperbolic Conservation Laws Near Critical States Reyna Quita Yu-Shuo Chen Hsin-Yi Lee John M. Hong PINN 11 0 0 22 May 2023
$PINNs error estimates for nonlinear equations in $\mathbb{R}$-smooth Banach spaces$ PINNs error estimates for nonlinear equations in $\mathbb{R}$ -smooth Banach spaces Jiexing Gao Yurii Zakharian 28 1 0 18 May 2023
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines Kamaljyoti Nath Xuhui Meng Daniel J. Smith George Karniadakis PINN 28 20 0 26 Apr 2023
Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity Ali Kashefi Leonidas J. Guibas T. Mukerji PINN 3DPC AI4CE 32 9 0 22 Mar 2023
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions Michael Penwarden Ameya Dilip Jagtap Shandian Zhe George Karniadakis Robert M. Kirby PINN AI4CE 23 57 0 28 Feb 2023
Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data D. Anton Henning Wessels AI4CE 36 7 0 15 Dec 2022
Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing Salah A. Faroughi N. Pawar C. Fernandes Maziar Raissi Subasish Das N. Kalantari S. K. Mahjour PINN AI4CE 27 49 0 14 Nov 2022
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks Shibo Li Michael Penwarden Yiming Xu Conor Tillinghast Akil Narayan Robert M. Kirby Shandian Zhe AI4CE 21 4 0 23 Oct 2022
A Dimension-Augmented Physics-Informed Neural Network (DaPINN) with High Level Accuracy and Efficiency Weilong Guan Kai-Ping Yang Yinsheng Chen Zhong Guan PINN AI4CE 18 12 0 19 Oct 2022
A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks S. Dong Yiran Wang 29 20 0 09 Oct 2022
A deep learning approach to solve forward differential problems on graphs Yuanyuan Zhao Massimiliano Lupo Pasini 17 0 0 07 Oct 2022
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 56 24 0 03 Oct 2022
How important are activation functions in regression and classification? A survey, performance comparison, and future directions Ameya Dilip Jagtap George Karniadakis AI4CE 37 71 0 06 Sep 2022
Error estimates for physics informed neural networks approximating the Navier-Stokes equations Tim De Ryck Ameya Dilip Jagtap S. Mishra PINN 49 115 0 17 Mar 2022
Parallel Physics-Informed Neural Networks via Domain Decomposition K. Shukla Ameya Dilip Jagtap George Karniadakis PINN 101 274 0 20 Apr 2021
hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition E. Kharazmi Zhongqiang Zhang George Karniadakis 128 509 0 11 Mar 2020