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ORCIDGeorge Em Karniadakis
George E. Karniadakis
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- 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)