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George Em Karniadakis
George E. Karniadakis
Person information

- affiliation: Brown University, Division of Applied Mathematics, Providence, RI, USA
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2020 – today
- 2023
- [j187]Patricio Clark Di Leoni
, Lu Lu
, Charles Meneveau, George Em Karniadakis, Tamer A. Zaki
:
Neural operator prediction of linear instability waves in high-speed boundary layers. J. Comput. Phys. 474: 111793 (2023) - [j186]Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis:
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons. J. Comput. Phys. 477: 111902 (2023) - [j185]Katiana Kontolati, Somdatta Goswami
, Michael D. Shields
, George Em Karniadakis:
On the influence of over-parameterization in manifold based surrogates and deep neural operators. J. Comput. Phys. 479: 112008 (2023) - [j184]Ahmad Peyvan, Khemraj Shukla, Jesse Chan
, George E. Karniadakis:
High-order methods for hypersonic flows with strong shocks and real chemistry. J. Comput. Phys. 490: 112310 (2023) - [j183]Yeonjong Shin
, Jérôme Darbon, George Em Karniadakis:
Accelerating gradient descent and Adam via fractional gradients. Neural Networks 161: 185-201 (2023) - [i121]Zongren Zou, George Em Karniadakis:
L-HYDRA: Multi-Head Physics-Informed Neural Networks. CoRR abs/2301.02152 (2023) - [i120]QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis:
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling. CoRR abs/2301.11402 (2023) - [i119]Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis:
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils. CoRR abs/2302.00807 (2023) - [i118]Aniruddha Bora
, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis:
Learning bias corrections for climate models using deep neural operators. CoRR abs/2302.03173 (2023) - [i117]Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis:
Learning stiff chemical kinetics using extended deep neural operators. CoRR abs/2302.12645 (2023) - [i116]Michael Penwarden, Ameya D. Jagtap, Shandian Zhe, George Em Karniadakis, Robert M. Kirby:
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. CoRR abs/2302.14227 (2023) - [i115]Katarzyna Michalowska, Somdatta Goswami, George Em Karniadakis, Signe Riemer-Sørensen:
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks. CoRR abs/2303.02243 (2023) - [i114]Oded Ovadia, Adar Kahana, Panos Stinis, Eli Turkel, George Em Karniadakis:
ViTO: Vision Transformer-Operator. CoRR abs/2303.08891 (2023) - [i113]Qianying Cao, Somdatta Goswami, George Em Karniadakis:
LNO: Laplace Neural Operator for Solving Differential Equations. CoRR abs/2303.10528 (2023) - [i112]Lei Ma, Rong xin Li, Fanhai Zeng, Ling Guo, George Em Karniadakis:
Bi-orthogonal fPINN: A physics-informed neural network method for solving time-dependent stochastic fractional PDEs. CoRR abs/2303.10913 (2023) - [i111]Paula Chen
, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis:
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems. CoRR abs/2303.12928 (2023) - [i110]Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields:
Learning in latent spaces improves the predictive accuracy of deep neural operators. CoRR abs/2304.07599 (2023) - [i109]Simin Shekarpaz, Fanhai Zeng, George Em Karniadakis:
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models. CoRR abs/2304.13205 (2023) - [i108]Kamaljyoti Nath, Xuhui Meng, Daniel J. Smith, George Em Karniadakis:
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines. CoRR abs/2304.13799 (2023) - [i107]Minglang Yin, Zongren Zou, Enrui Zhang, Cristina Cavinato, Jay D. Humphrey, George Em Karniadakis:
A Generative Modeling Framework for Inferring Families of Biomechanical Constitutive Laws in Data-Sparse Regimes. CoRR abs/2305.03184 (2023) - [i106]Elham Kiyani, Khemraj Shukla, George Em Karniadakis, Mikko Karttunen:
A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data. CoRR abs/2305.10706 (2023) - [i105]Chayan Banerjee
, Kien Nguyen, Clinton Fookes, George Em Karniadakis:
Physics-Informed Computer Vision: A Review and Perspectives. CoRR abs/2305.18035 (2023) - [i104]Ehsan Haghighat, Umair bin Waheed, George Em Karniadakis:
A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization. CoRR abs/2306.04096 (2023) - [i103]Varun Kumar
, Leonard Gleyzer, Adar Kahana, Khemraj Shukla, George Em Karniadakis:
CrunchGPT: A chatGPT assisted framework for scientific machine learning. CoRR abs/2306.15551 (2023) - [i102]Alena Kopanicáková, Hardik Kothari
, George Em Karniadakis, Rolf Krause:
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies. CoRR abs/2306.17648 (2023) - [i101]Sokratis J. Anagnostopoulos, Juan Diego Toscano, Nikolaos Stergiopulos, George Em Karniadakis:
Residual-based attention and connection to information bottleneck theory in PINNs. CoRR abs/2307.00379 (2023) - [i100]Alan John Varghese, Aniruddha Bora, Mengjia Xu, George Em Karniadakis:
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers. CoRR abs/2307.02588 (2023) - [i99]Zhen Zhang, Zongren Zou, Ellen Kuhl, George Em Karniadakis:
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression. CoRR abs/2307.08107 (2023) - [i98]Oded Ovadia, Eli Turkel, Adar Kahana, George Em Karniadakis:
DiTTO: Diffusion-inspired Temporal Transformer Operator. CoRR abs/2307.09072 (2023) - [i97]Elham Kiyani, Mahdi Kooshkbaghi, Khemraj Shukla, Rahul Babu Koneru, Zhen Li, Luis Bravo, Anindya Ghoshal, George Em Karniadakis, Mikko Karttunen:
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs. CoRR abs/2307.09142 (2023) - [i96]Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi:
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks. CoRR abs/2307.12306 (2023) - [i95]Nikolas Borrel-Jensen, Somdatta Goswami, Allan P. Engsig-Karup, George Em Karniadakis, Cheol-Ho Jeong:
Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators. CoRR abs/2308.05141 (2023) - [i94]Qian Zhang, Chenxi Wu, Adar Kahana, Youngeun Kim, Yuhang Li, George Em Karniadakis, Priyadarshini Panda:
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning. CoRR abs/2308.16372 (2023) - 2022
- [j182]Ameya D. Jagtap, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis:
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. Neurocomputing 468: 165-180 (2022) - [j181]Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis
, George Em Karniadakis:
Learning functional priors and posteriors from data and physics. J. Comput. Phys. 457: 111073 (2022) - [j180]Apostolos F. Psaros
, Kenji Kawaguchi, George Em Karniadakis:
Meta-learning PINN loss functions. J. Comput. Phys. 458: 111121 (2022) - [j179]Yue Zhao, Zhiping Mao, Ling Guo, Yifa Tang, George Em Karniadakis:
A spectral method for stochastic fractional PDEs using dynamically-orthogonal/bi-orthogonal decomposition. J. Comput. Phys. 461: 111213 (2022) - [j178]Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, George Em Karniadakis:
Physics-informed neural networks for inverse problems in supersonic flows. J. Comput. Phys. 466: 111402 (2022) - [j177]Somdatta Goswami
, Katiana Kontolati
, Michael D. Shields
, George Em Karniadakis
:
Deep transfer operator learning for partial differential equations under conditional shift. Nat. Mac. Intell. 4(12): 1155-1164 (2022) - [j176]Beichuan Deng
, Yeonjong Shin, Lu Lu
, Zhongqiang Zhang
, George Em Karniadakis
:
Approximation rates of DeepONets for learning operators arising from advection-diffusion equations. Neural Networks 153: 411-426 (2022) - [j175]He Li, Yixiang Deng, Konstantina Sampani, Shengze Cai, Zhen Li, Jennifer K. Sun, George E. Karniadakis:
Computational investigation of blood cell transport in retinal microaneurysms. PLoS Comput. Biol. 18(1) (2022) - [j174]He Li, Yixiang Deng, Zhen Li, Ander Dorken Gallastegi, Christos S. Mantzoros, Galit H. Frydman, George E. Karniadakis:
Multiphysics and multiscale modeling of microthrombosis in COVID-19. PLoS Comput. Biol. 18(3) (2022) - [j173]Enrui Zhang
, Bart Spronck
, Jay D. Humphrey
, George Em Karniadakis
:
G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning. PLoS Comput. Biol. 18(10): 1010660 (2022) - [j172]Liu Yang
, Constantinos Daskalakis, George E. Karniadakis
:
Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models. SIAM J. Sci. Comput. 44(1): 80- (2022) - [j171]Zheyuan Hu
, Ameya D. Jagtap
, George Em Karniadakis
, Kenji Kawaguchi:
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? SIAM J. Sci. Comput. 44(5): 3158- (2022) - [j170]Tingwei Meng
, Zhen Zhang, Jérôme Darbon, George E. Karniadakis
:
SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems. SIAM J. Sci. Comput. 44(6): 1341- (2022) - [j169]Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman, George Em Karniadakis:
A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems. IEEE Signal Process. Mag. 39(1): 68-77 (2022) - [j168]Liu Yang
, George Em Karniadakis
:
Potential Flow Generator With L2 Optimal Transport Regularity for Generative Models. IEEE Trans. Neural Networks Learn. Syst. 33(2): 528-538 (2022) - [i93]Tingwei Meng, Zhen Zhang, Jérôme Darbon, George Em Karniadakis:
SympOCnet: Solving optimal control problems with applications to high-dimensional multi-agent path planning problems. CoRR abs/2201.05475 (2022) - [i92]Apostolos F. Psaros, Xuhui Meng, Zongren Zou, Ling Guo, George Em Karniadakis:
Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons. CoRR abs/2201.07766 (2022) - [i91]Mitchell Daneker, Zhen Zhang, George Em Karniadakis, Lu Lu:
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks. CoRR abs/2202.01723 (2022) - [i90]Ameya D. Jagtap, Dimitrios Mitsotakis, George Em Karniadakis:
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre-Green-Naghdi equations. CoRR abs/2202.02899 (2022) - [i89]Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna C. Garikipati, Lori Graham-Brady, Amanda A. Howard, George Em Karniadakis, Vahid Keshavarzzadeh, Robert M. Kirby, J. Nathan Kutz, Chunhui Li
, Xing Liu, Hannah Lu, Pania Newell, Daniel O'Malley, Masa Prodanovic, Gowri Srinivasan, Alexandre M. Tartakovsky, Daniel M. Tartakovsky, Hamdi A. Tchelepi, Bozo Vazic, Hari S. Viswanathan, Hongkyu Yoon, Piotr Zarzycki:
Machine Learning in Heterogeneous Porous Materials. CoRR abs/2202.04137 (2022) - [i88]Ameya D. Jagtap, Zhiping Mao, Nikolaus Adams, George Em Karniadakis:
Physics-informed neural networks for inverse problems in supersonic flows. CoRR abs/2202.11821 (2022) - [i87]Minglang Yin, Enrui Zhang, Yue Yu, George Em Karniadakis:
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems. CoRR abs/2203.00003 (2022) - [i86]Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis:
On the influence of over-parameterization in manifold based surrogates and deep neural operators. CoRR abs/2203.05071 (2022) - [i85]Ethan Pickering, George Em Karniadakis, Themistoklis P. Sapsis:
Discovering and forecasting extreme events via active learning in neural operators. CoRR abs/2204.02488 (2022) - [i84]Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, George Em Karniadakis:
Learning two-phase microstructure evolution using neural operators and autoencoder architectures. CoRR abs/2204.07230 (2022) - [i83]Amanda A. Howard
, Mauro Perego, George E. Karniadakis, Panos Stinis:
Multifidelity Deep Operator Networks. CoRR abs/2204.09157 (2022) - [i82]Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis:
Deep transfer learning for partial differential equations under conditional shift with DeepONet. CoRR abs/2204.09810 (2022) - [i81]Somdatta Goswami, David S. Li
, Bruno V. Rego, Marcos Latorre, Jay D. Humphrey, George Em Karniadakis:
Neural operator learning of heterogeneous mechanobiological insults contributing to aortic aneurysms. CoRR abs/2205.03780 (2022) - [i80]Kevin Linka, Amelie Schäfer, Xuhui Meng, Zongren Zou, George Em Karniadakis, Ellen Kuhl
:
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems. CoRR abs/2205.08304 (2022) - [i79]Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis:
Scalable algorithms for physics-informed neural and graph networks. CoRR abs/2205.08332 (2022) - [i78]Adar Kahana, Qian Zhang, Leonard Gleyzer, George Em Karniadakis:
Function Regression using Spiking DeepONet. CoRR abs/2205.10130 (2022) - [i77]Min Cai, George Em Karniadakis, Changpin Li:
Fractional SEIR Model and Data-Driven Predictions of COVID-19 Dynamics of Omicron Variant. CoRR abs/2205.11379 (2022) - [i76]Somdatta Goswami, Aniruddha Bora
, Yue Yu, George Em Karniadakis:
Physics-Informed Deep Neural Operator Networks. CoRR abs/2207.05748 (2022) - [i75]Enrui Zhang, Bart Spronck, Jay D. Humphrey, George Em Karniadakis:
G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning. CoRR abs/2208.09889 (2022) - [i74]Zongren Zou, Xuhui Meng, Apostolos F. Psaros, George Em Karniadakis:
NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators. CoRR abs/2208.11866 (2022) - [i73]Enrui Zhang, Adar Kahana, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis:
A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods. CoRR abs/2208.13273 (2022) - [i72]Ameya D. Jagtap, George Em Karniadakis:
How important are activation functions in regression and classification? A survey, performance comparison, and future directions. CoRR abs/2209.02681 (2022) - [i71]Adar Kahana, Enrui Zhang, Somdatta Goswami, George Em Karniadakis, Rishikesh Ranade, Jay Pathak:
On the Geometry Transferability of the Hybrid Iterative Numerical Solver for Differential Equations. CoRR abs/2210.17392 (2022) - [i70]Zheyuan Hu
, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi:
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. CoRR abs/2211.08939 (2022) - [i69]Qian Zhang, Adar Kahana, George Em Karniadakis, Panos Stinis:
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations. CoRR abs/2211.09928 (2022) - [i68]Ahmad Peyvan, Khemraj Shukla, Jesse Chan, George Em Karniadakis:
High-Order Methods for Hypersonic Flows with Strong Shocks and Real Chemistry. CoRR abs/2211.12635 (2022) - [i67]Min Zhu, Handi Zhang, Anran Jiao, George Em Karniadakis, Lu Lu:
Reliable extrapolation of deep neural operators informed by physics or sparse observations. CoRR abs/2212.06347 (2022) - 2021
- [j167]Max Carlson, Xiaoning Zheng, Hari Sundar, George Em Karniadakis, Robert M. Kirby:
An open-source parallel code for computing the spectral fractional Laplacian on 3D complex geometry domains. Comput. Phys. Commun. 261: 107695 (2021) - [j166]Fangying Song, George Em Karniadakis:
Variable-Order Fractional Models for Wall-Bounded Turbulent Flows. Entropy 23(6): 782 (2021) - [j165]Liu Yang, Xuhui Meng, George Em Karniadakis:
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data. J. Comput. Phys. 425: 109913 (2021) - [j164]Xiaowei Jin
, Shengze Cai
, Hui Li
, George Em Karniadakis:
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations. J. Comput. Phys. 426: 109951 (2021) - [j163]Lifei Zhao, Zhen Li
, Zhicheng Wang, Bruce Caswell, Jie Ouyang, George Em Karniadakis:
Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows. J. Comput. Phys. 427: 110069 (2021) - [j162]Zhicheng Wang, Xiaoning Zheng, Chryssostomos Chryssostomidis, George Em Karniadakis:
A phase-field method for boiling heat transfer. J. Comput. Phys. 435: 110239 (2021) - [j161]Shengze Cai, Zhicheng Wang, Lu Lu
, Tamer A. Zaki, George Em Karniadakis:
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks. J. Comput. Phys. 436: 110296 (2021) - [j160]Xuhui Meng, Hessam Babaee, George Em Karniadakis:
Multi-fidelity Bayesian neural networks: Algorithms and applications. J. Comput. Phys. 438: 110361 (2021) - [j159]Qin Lou, Xuhui Meng, George Em Karniadakis:
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation. J. Comput. Phys. 447: 110676 (2021) - [j158]Khemraj Shukla
, Ameya D. Jagtap
, George Em Karniadakis:
Parallel physics-informed neural networks via domain decomposition. J. Comput. Phys. 447: 110683 (2021) - [j157]Zhiping Mao, Lu Lu
, Olaf Marxen, Tamer A. Zaki, George Em Karniadakis:
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators. J. Comput. Phys. 447: 110698 (2021) - [j156]Lu Lu
, Pengzhan Jin
, Guofei Pang, Zhongqiang Zhang
, George Em Karniadakis
:
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3(3): 218-229 (2021) - [j155]Yixiang Deng
, Lu Lu
, Laura Aponte
, Angeliki M. Angelidi
, Vera Novak, George Em Karniadakis
, Christos S. Mantzoros
:
Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. npj Digit. Medicine 4 (2021) - [j154]Sheng Zhang
, Joan Ponce
, Zhen Zhang
, Guang Lin
, George E. Karniadakis
:
An integrated framework for building trustworthy data-driven epidemiological models: Application to the COVID-19 outbreak in New York City. PLoS Comput. Biol. 17(9) (2021) - [j153]He Li
, Zixiang Leonardo Liu
, Lu Lu
, Pierre Buffet, George Em Karniadakis
:
How the spleen reshapes and retains young and old red blood cells: A computational investigation. PLoS Comput. Biol. 17(11) (2021) - [j152]Lu Lu
, Xuhui Meng, Zhiping Mao, George Em Karniadakis
:
DeepXDE: A Deep Learning Library for Solving Differential Equations. SIAM Rev. 63(1): 208-228 (2021) - [j151]Xiaoli Chen
, Liu Yang
, Jinqiao Duan
, George Em Karniadakis
:
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-Informed Neural Networks. SIAM J. Sci. Comput. 43(3): B811-B830 (2021) - [c25]Ameya D. Jagtap, George E. Karniadakis:
Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations. AAAI Spring Symposium: MLPS 2021 - [i66]Min Cai, Ehsan Kharazmi, Changpin Li, George Em Karniadakis:
Fractional Buffer Layers: Absorbing Boundary Conditions for Wave Propagation. CoRR abs/2101.02355 (2021) - [i65]Liu Yang, Tingwei Meng, George Em Karniadakis:
Measure-conditional Discriminator with Stationary Optimum for GANs and Statistical Distance Surrogates. CoRR abs/2101.06802 (2021) - [i64]Samuel Lanthaler, Siddhartha Mishra, George Em Karniadakis:
Error estimates for DeepOnets: A deep learning framework in infinite dimensions. CoRR abs/2102.09618 (2021) - [i63]Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis:
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations. CoRR abs/2102.10621 (2021) - [i62]Yeonjong Shin, Jérôme Darbon, George Em Karniadakis:
A Caputo fractional derivative-based algorithm for optimization. CoRR abs/2104.02259 (2021) - [i61]Khemraj Shukla, Ameya D. Jagtap
, George Em Karniadakis:
Parallel Physics-Informed Neural Networks via Domain Decomposition. CoRR abs/2104.10013 (2021) - [i60]Shengze Cai, Zhiping Mao, Zhicheng Wang, Minglang Yin, George Em Karniadakis:
Physics-informed neural networks (PINNs) for fluid mechanics: A review. CoRR abs/2105.09506 (2021) - [i59]Ameya D. Jagtap
, Yeonjong Shin, Kenji Kawaguchi, George Em Karniadakis:
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. CoRR abs/2105.09513 (2021) - [i58]Qian Zhang, Konstantina Sampani, Mengjia Xu, Shengze Cai, Yixiang Deng, He Li, Jennifer K. Sun, George Em Karniadakis:
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images. CoRR abs/2106.02800 (2021) - [i57]Xuhui Meng, Liu Yang, Zhiping Mao, José del Águila Ferrandis, George Em Karniadakis:
Learning Functional Priors and Posteriors from Data and Physics. CoRR abs/2106.05863 (2021) - [i56]Apostolos F. Psaros
, Kenji Kawaguchi, George Em Karniadakis:
Meta-learning PINN loss functions. CoRR abs/2107.05544 (2021) - [i55]Somdatta Goswami, Minglang Yin, Yue Yu, George E. Karniadakis:
A physics-informed variational DeepONet for predicting the crack path in brittle materials. CoRR abs/2108.06905 (2021) - [i54]Minglang Yin, Ehsan Ban, Bruno V. Rego, Enrui Zhang, Cristina Cavinato, Jay D. Humphrey, George Em Karniadakis:
Simulating progressive intramural damage leading to aortic dissection using an operator-regression neural network. CoRR abs/2108.11985 (2021) - [i53]Zhen Zhang, Yeonjong Shin, George Em Karniadakis:
GFINNs: GENERIC Formalism Informed Neural Networks for Deterministic and Stochastic Dynamical Systems. CoRR abs/2109.00092 (2021) - [i52]Zheyuan Hu, Ameya D. Jagtap
, George Em Karniadakis, Kenji Kawaguchi:
When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization? CoRR abs/2109.09444 (2021) - [i51]Mengjia Xu, Apoorva Vikram Singh, George Em Karniadakis:
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs. CoRR abs/2109.13441 (2021) - [i50]Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis:
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. CoRR abs/2111.02801 (2021) - 2020
- [j150]Xuhui Meng, George Em Karniadakis:
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems. J. Comput. Phys. 401 (2020) - [j149]Ameya D. Jagtap
, Kenji Kawaguchi, George Em Karniadakis:
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. J. Comput. Phys. 404 (2020)