PETAL: Physics Emulation Through Averaged Linearizations for Solving Inverse Problems

Jihui Jin, Etienne Ollivier, Richard X. Touret, Matthew McKinley, Karim G. Sabra, Justin Romberg

Type: Preprint

Publication Date: 2023-01-01

Citations: 2

DOI: https://doi.org/10.48550/arxiv.2305.11056

View Publication

Locations

arXiv (Cornell University) - View - PDF
DataCite API - View

Similar Works

Action	Title	Year	Authors
+ PDF Chat	TAE: A Model-Constrained Tikhonov Autoencoder Approach for Forward and Inverse Problems	2024	Hai V. Nguyen Tan Bui‐Thanh
+ PDF Chat	Taen: A Model-Constrained Tikhonov Autoencoder Network for Forward and Inverse Problems	2025	Hai V. Nguyen Tan Bui‐Thanh Clint Dawson
+ PDF Chat	Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures	2024	Peimeng Guan Naveed Iqbal Mark A. Davenport Mudassir Masood
+	Accelerating Least Squares Imaging Using Deep Learning Techniques	2019	Janaki Vamaraju Jeremy Vila Mauricio Araya‐Polo Debanjan Datta Mohamed Sid‐Ahmed Mrinal K. Sen
+	Learned multiphysics inversion with differentiable programming and machine learning	2023	Mathias Louboutin Ziyi Yin Rafael Orozco Thomas J. Grady Ali Siahkoohi Gabrio Rizzuti Philipp Witte Olav Møyner Gerard Gorman Felix J. Herrmann
+ PDF Chat	Learning in Sinusoidal Spaces with Physics-Informed Neural Networks	2021	Jian Cheng Wong Chin Chun Ooi Abhishek Gupta Yew-Soon Ong
+	Learning in Sinusoidal Spaces with Physics-Informed Neural Networks	2021	Jian Cheng Wong Chinchun Ooi Abhishek Gupta Yew-Soon Ong
+ PDF Chat	Learning in Sinusoidal Spaces with Physics-Informed Neural Networks	2022	Jian Cheng Wong Chin Chun Ooi Abhishek Gupta Yew-Soon Ong
+	Simpinns: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems	2023	Sidney Besnard Frédéric Jurie Jalal Fadili
+	Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems	2023	Yifei Zong David A. Barajas‐Solano Alexandre M. Tartakovsky
+ PDF Chat	Learned multiphysics inversion with differentiable programming and machine learning	2023	Mathias Louboutin Ziyi Yin Rafael Orozco Thomas J. Grady Ali Siahkoohi Gabrio Rizzuti Philipp Witte Olav Møyner Gerard Gorman Felix J. Herrmann
+	Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations	2020	Kailai Xu Eric Darve
+ PDF Chat	Physics Constrained Learning for Data-driven Inverse Modeling from Sparse Observations	2020	Kailai Xu Eric Darve
+	Enhancing Gravity Currents Analysis through Physics-Informed Neural Networks: Insights from Experimental Observations	2023	Mickaël G. Delcey Yoann Cheny Jean Schneider Simon Becker Yvan Dossmann Sébastien Kiesgen de Richter
+	Physics constrained learning for data-driven inverse modeling from sparse observations	2022	Kailai Xu Eric Darve
+	Eikonal solution using physics-informed neural networks	2020	Umair bin Waheed Ehsan Haghighat Tariq Alkhalifah Chao Song Qi Hao
+ PDF Chat	Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field	2022	R. Iqbal Amir Behjat Revant Adlakha Jesse Callanan Mostafa Nouh Souma Chowdhury
+ PDF Chat	Multi-Frequency Progressive Refinement for Learned Inverse Scattering	2024	Owen Melia Olivia Tsang Vasileios Charisopoulos Yuehaw Khoo Jeremy G. Hoskins Rebecca Willett
+ PDF Chat	Data-Aided Underwater Acoustic Ray Propagation Modeling	2023	Kexin Li Mandar Chitre
+	Efficient Training of Transfer Mapping in Physics-Infused Machine Learning Models of UAV Acoustic Field	2022	R. Iqbal Amir Behjat Revant Adlakha Jesse Callanan Mostafa Nouh Souma Chowdhury

Works That Cite This (1)

Action	Title	Year	Authors
+ PDF Chat	Regularization by Deep Learning in Signal Processing	2023	Carlos Ramirez Villamarin Erwin Suazo Tamer Oraby

Works Cited by This (0)

Action	Title	Year	Authors