Fetching the paper…
Reading the bibliography…
Physics-informed neural networks (PINNs) are a new tool for solving boundary value problems by defining loss functions of neural networks based on governing equations, boundary conditions, and initial conditions.
Staggered transient analysis procedures for coupled mechanical systems: formulation
Carlos A Felippa and Kwang-Chun Park · 1980
Earlier work this paper cites.
FEAP - finite element analysis program, 2014
R. L. Taylor · 2014
Earlier work this paper cites.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Earlier work this paper cites.
{ \{ TensorFlow
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
Earlier work this paper cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Earlier work this paper cites.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
Earlier work this paper cites.
A review of the application of machine learning and data mining approaches in continuum materials mechanics
Frederic E. Bock, Roland C. Aydin, Christian J. Cyron, Norbert Huber, Surya R. Kalidindi, and Benjamin Klusemann · 2019
Earlier work this paper cites.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
M. Raissi, P. Perdikaris, and G.E. Karniadakis · 2019
Earlier work this paper cites.
Comparison of microstructure features and mechanical properties for additive manufactured and wrought nickel alloys 625
J. Nguejio, F. Szmytka, S. Hallais, A. Tanguy, S. Nardone, and M. Godino Martinez · 2019
Earlier work this paper cites.
Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020
Earlier work this paper cites.
Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior
Mauricio Fernández, Shahed Rezaei, Jaber Rezaei Mianroodi, Felix Fritzen, and Stefanie Reese · 2020
Earlier work this paper cites.
Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model
Bowen Wang, Guobin Zhang, Huizhi Wang, Jin Xuan, and Kui Jiao · 2020
Earlier work this paper cites.
Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations
Ameya D Jagtap and George E Karniadakis · 2020
Earlier work this paper cites.
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
Ameya D. Jagtap, Ehsan Kharazmi, and George Em Karniadakis · 2020
Earlier work this paper cites.
Extended physics-informed neural networks (xpinns): A generalized space-time domain decomposition based deep learning framework for nonlinear partial differential equations
Ameya D. Jagtap and George Em Karniadakis · 2020
Earlier work this paper cites.
Self-adaptive physics-informed neural networks using a soft attention mechanism
Levi McClenny and Ulisses Braga-Neto · 2020
Earlier work this paper cites.
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
E. Samaniego, C. Anitescu, S. Goswami, V.M. Nguyen-Thanh, H. Guo, K. Hamdia, X. Zhuang, and T. Rabczuk · 2020
Earlier work this paper cites.
What is being transferred in transfer learning?
Behnam Neyshabur, Hanie Sedghi, and Chiyuan Zhang · 2020
Earlier work this paper cites.
Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
Somdatta Goswami, Cosmin Anitescu, Souvik Chakraborty, and Timon Rabczuk · 2020
Earlier work this paper cites.
Multiscale modeling meets machine learning: What can we learn?
Grace C. Y. Peng, Mark Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Savador Dura-Bernal, Krishna Garikipati, George Karniadakis, William W. Lytton, Paris Perdikaris, Linda Petzold, and Ellen Kuhl · 2021
Earlier work this paper cites.
Deep learning model to predict complex stress and strain fields in hierarchical composites
Zhenze Yang, Chi-Hua Yu, and Markus J. Buehler · 2021
Earlier work this paper cites.
Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling
Marco Ragone, Vitaliy Yurkiv, Ajaykrishna Ramasubramanian, Babak Kashir, and Farzad Mashayek · 2021
Earlier work this paper cites.
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
Ehsan Haghighat, Maziar Raissi, Adrian Moure, Hector Gomez, and Ruben Juanes · 2021
Cited alongside, same era.
Multi-objective loss balancing for physics-informed deep learning
Rafael Bischof and Michael Kraus · 2021
Cited alongside, same era.
On the pareto front of physics-informed neural networks
Franz M Rohrhofer, Stefan Posch, and Bernhard C Geiger · 2021
Cited alongside, same era.
A consistent framework for chemo-mechanical cohesive fracture and its application in solid-state batteries
Shahed Rezaei, Armin Asheri, and Bai-Xiang Xu · 2021
Cited alongside, same era.
Self-adaptive loss balanced physics-informed neural networks
Zixue Xiang, Wei Peng, Xu Liu, and Wen Yao · 2022
Later among the works it cites.
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
Jan N. Fuhg and Nikolaos Bouklas · 2022
Later among the works it cites.
Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis
Hongwei Guo, Xiaoying Zhuang, Pengwan Chen, Naif Alajlan, and Timon Rabczuk · 2022
Later among the works it cites.
A physics-informed variational deeponet for predicting crack path in quasi-brittle materials
Somdatta Goswami, Minglang Yin, Yue Yu, and George Em Karniadakis · 2022
Later among the works it cites.
Physics-informed neural network solution of thermo–hydro–mechanical processes in porous media
Danial Amini, Ehsan Haghighat, and Ruben Juanes · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Mayank Raj, Pramod Yallappa Kumbhar, and Ratna Kumar Annabattula · 2021
Cited alongside, same era.
Simulation of multi-species flow and heat transfer using physics-informed neural networks
R. Laubscher · 2021
Cited alongside, same era.
A comprehensive survey on transfer learning
Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He · 2021
Cited alongside, same era.
One-shot transfer learning of physics-informed neural networks
Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, and Stephen Roberts · 2021
Cited alongside, same era.
Sciann: A keras/tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
Ehsan Haghighat and Ruben Juanes · 2021
Cited alongside, same era.
Parallel physics-informed neural networks via domain decomposition
Khemraj Shukla, Ameya D. Jagtap, and George Em Karniadakis · 2021
Cited alongside, same era.
hp-vpinns: Variational physics-informed neural networks with domain decomposition
Ehsan Kharazmi, Zhongqiang Zhang, and George E.M. Karniadakis · 2021
Cited alongside, same era.
Meshless physics-informed deep learning method for three-dimensional solid mechanics
Diab W. Abueidda, Qiyue Lu, and Seid Koric · 2021
Cited alongside, same era.
A novel sequential method to train physics informed neural networks for allen cahn and cahn hilliard equations
Revanth Mattey and Susanta Ghosh · 2022
Later among the works it cites.
A critical evaluation of using physics-informed neural networks for simulating voltammetry: Strengths, weaknesses and best practices
Haotian Chen, Christopher Batchelor-McAuley, Enno Kätelhön, Joseph Elliott, and Richard G. Compton · 2022
Later among the works it cites.
Efficient physics informed neural networks coupled with domain decomposition methods for solving coupled multi-physics problems
Long Nguyen, Maziar Raissi, and Padmanabhan Seshaiyer · 2022
Later among the works it cites.
Physics-informed machine learning model for computational fracture of quasi-brittle materials without labelled data
Bin Zheng, Tongchun Li, Huijun Qi, Lingang Gao, Xiaoqing Liu, and Li Yuan · 2022
Later among the works it cites.
A transfer learning-physics informed neural network (tl-pinn) for vortex-induced vibration
Hesheng Tang, Yangyang Liao, Hu Yang, and Liyu Xie · 2022
Later among the works it cites.
Transfer learning with physics-informed neural networks for efficient simulation of branched flows, 2022
Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis, and Pavlos Protopapas · 2022
Later among the works it cites.
Svd-pinns: Transfer learning of physics-informed neural networks via singular value decomposition, 2022
Yihang Gao, Ka Chun Cheung, and Michael K. Ng · 2022
Later among the works it cites.
How important are activation functions in regression and classification? a survey, performance comparison, and future directions, 2022
Ameya D. Jagtap and George Em Karniadakis · 2022
Later among the works it cites.
J. Abbasi and P. Andersen · 2022
Later among the works it cites.
Physics-informed neural network simulation of multiphase poroelasticity using stress-split sequential training
Ehsan Haghighat, Danial Amini, and Ruben Juanes · 2022
Later among the works it cites.
Can-pinn: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, and Yew-Soon Ong · 2022
Later among the works it cites.
Enhanced physics-informed neural networks for hyperelasticity
Diab W. Abueidda, Seid Koric, Erman Guleryuz, and Nahil A. Sobh · 2023
Closest in time.
A thermo-mechanical phase-field fracture model: Application to hot cracking simulations in additive manufacturing
Hui Ruan, Shahed Rezaei, Yangyiwei Yang, Dietmar Gross, and Bai-Xiang Xu · 2023
Closest in time.
Deep learning phase-field model for brittle fractures
Yousef Ghaffari Motlagh, Peter K. Jimack, and René de Borst · 2023
Closest in time.
Wave equation modeling via physics-informed neural networks: Models of soft and hard constraints for initial and boundary conditions
Shaikhah Alkhadhr and Mohamed Almekkawy · 2023
Closest in time.
Modeling finite-strain plasticity using physics-informed neural network and assessment of the network performance
Sijun Niu, Enrui Zhang, Yuri Bazilevs, and Vikas Srivastava · 2023
Closest in time.