George Em Karniadakis

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 185

Top Venues

Publications

Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks. CoRR (2024)
Varun Kumar, Somdatta Goswami, Daniel Smith, George Em Karniadakis
Real-time prediction of gas flow dynamics in diesel engines using a deep neural operator framework. Appl. Intell. 54 (1) (2024)
Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi
Tackling the curse of dimensionality with physics-informed neural networks. Neural Networks 176 (2024)
Khemraj Shukla, Vivek Oommen, Ahmad Peyvan, Michael Penwarden, Nicholas Plewacki, Luis Bravo, Anindya Ghoshal, Robert M. Kirby, George Em Karniadakis
Deep neural operators as accurate surrogates for shape optimization. Eng. Appl. Artif. Intell. 129 (2024)
Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang, George Em Karniadakis
Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks. CoRR (2024)
Zongren Zou, Xuhui Meng, George Em Karniadakis
Correcting model misspecification in physics-informed neural networks (PINNs). J. Comput. Phys. 505 (2024)
Zheyuan Hu, Zhongqiang Zhang, George Em Karniadakis, Kenji Kawaguchi
Score-fPINN: Fractional Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck-Levy Equations. CoRR (2024)
Zongren Zou, Tingwei Meng, Paula X. Chen, Jérôme Darbon, George Em Karniadakis
Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning. CoRR (2024)
Bradley H. Theilman, Qian Zhang, Adar Kahana, Eric C. Cyr, Nathaniel Trask, James B. Aimone, George Em Karniadakis
Spiking Physics-Informed Neural Networks on Loihi 2. NICE (2024)
Juan Diego Toscano, Theo Käufer, Zhibo Wang, Martin Maxey, Christian Cierpka, George Em Karniadakis
Inferring turbulent velocity and temperature fields and their statistics from Lagrangian velocity measurements using physics-informed Kolmogorov-Arnold Networks. CoRR (2024)
Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap, George Em Karniadakis
RiemannONets: Interpretable Neural Operators for Riemann Problems. CoRR (2024)
Paula X. Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning. L4DC (2024)
Zheyuan Hu, Zhongqiang Zhang, George Em Karniadakis, Kenji Kawaguchi
Score-Based Physics-Informed Neural Networks for High-Dimensional Fokker-Planck Equations. CoRR (2024)
Taorui Wang, Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang, George Em Karniadakis
Tensor neural networks for high-dimensional Fokker-Planck equations. CoRR (2024)
Alena Kopanicáková, George Em Karniadakis
DeepOnet Based Preconditioning Strategies For Solving Parametric Linear Systems of Equations. CoRR (2024)
Shupeng Wang, George Em Karniadakis
GMC-PINNs: A new general Monte Carlo PINNs method for solving fractional partial differential equations on irregular domains. CoRR (2024)
Qiao Zhuang, Chris Ziyi Yao, Zhongqiang Zhang, George Em Karniadakis
Two-scale Neural Networks for Partial Differential Equations with Small Parameters. CoRR (2024)
Shengze Cai, Callum Gray, George Em Karniadakis
Physics-Informed Neural Networks Enhanced Particle Tracking Velocimetry: An Example for Turbulent Jet Flow. IEEE Trans. Instrum. Meas. 73 (2024)
Zongren Zou, Adar Kahana, Enrui Zhang, Eli Turkel, Rishikesh Ranade, Jay Pathak, George Em Karniadakis
Large scale scattering using fast solvers based on neural operators. CoRR (2024)
Sokratis J. Anagnostopoulos, Juan Diego Toscano, Nikolaos Stergiopulos, George Em Karniadakis
Learning in PINNs: Phase transition, total diffusion, and generalization. CoRR (2024)
Khemraj Shukla, Zongren Zou, Chi Hin Chan, Additi Pandey, Zhicheng Wang, George Em Karniadakis
NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements. CoRR (2024)
Benjamin Shih, Ahmad Peyvan, Zhongqiang Zhang, George Em Karniadakis
Transformers as Neural Operators for Solutions of Differential Equations with Finite Regularity. CoRR (2024)
Jin Song, Ming Zhong, George Em Karniadakis, Zhenya Yan
Two-stage initial-value iterative physics-informed neural networks for simulating solitary waves of nonlinear wave equations. J. Comput. Phys. 505 (2024)
Mengjia Xu, Apoorva Vikram Singh, George Em Karniadakis
DynG2G: An Efficient Stochastic Graph Embedding Method for Temporal Graphs. IEEE Trans. Neural Networks Learn. Syst. 35 (1) (2024)
Alan John Varghese, Aniruddha Bora, Mengjia Xu, George Em Karniadakis
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers. Neural Networks 172 (2024)
Paula X. Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis
Leveraging Multitime Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems. SIAM J. Sci. Comput. 46 (2) (2024)
Qianying Cao, Somdatta Goswami, George Em Karniadakis
Laplace neural operator for solving differential equations. Nat. Mac. Intell. 6 (6) (2024)
Mario De Florio, Adar Kahana, George Em Karniadakis
Analysis of biologically plausible neuron models for regression with spiking neural networks. CoRR (2024)
Youngkyu Lee, Alena Kopanicáková, George Em Karniadakis
Two-level overlapping additive Schwarz preconditioner for training scientific machine learning applications. CoRR (2024)
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 (2023)
Guansheng Li, Yuhao Qiang, He Li, Xuejin Li, Pierre Buffet, Ming Dao, George Em Karniadakis
A combined computational and experimental investigation of the filtration function of splenic macrophages in sickle cell disease. PLoS Comput. Biol. 19 (12) (2023)
Nazanin Ahmadi Daryakenari, Mario De Florio, Khemraj Shukla, George Em Karniadakis
AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification. CoRR (2023)
Zheyuan Hu, Ameya D. Jagtap, George Em Karniadakis, Kenji Kawaguchi
Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology. Eng. Appl. Artif. Intell. 126 (2023)
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 (2023)
Zongren Zou, Xuhui Meng, George Em Karniadakis
Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators. CoRR (2023)
QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis
A hybrid deep neural operator/finite element method for ice-sheet modeling. J. Comput. Phys. 492 (2023)
Bin Lin, Zhiping Mao, Zhicheng Wang, George Em Karniadakis
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions. CoRR (2023)
Ehsan Haghighat, Umair bin Waheed, George Em Karniadakis
A novel deeponet model for learning moving-solution operators with applications to earthquake hypocenter localization. CoRR (2023)
Katarzyna Michalowska, Somdatta Goswami, George Em Karniadakis, Signe Riemer-Sørensen
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks. CoRR (2023)
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 (2023)
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 (2023)
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 (2023)
Chenxi Wu, Alan John Varghese, Vivek Oommen, George Em Karniadakis
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science. CoRR (2023)
Qianying Cao, Somdatta Goswami, George Em Karniadakis
LNO: Laplace Neural Operator for Solving Differential Equations. CoRR (2023)
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 (2023)
Amanda A. Howard, Mauro Perego, George Em Karniadakis, Panos Stinis
Multifidelity deep operator networks for data-driven and physics-informed problems. J. Comput. Phys. 493 (2023)
Aniruddha Bora, Khemraj Shukla, Shixuan Zhang, Bryce Harrop, Ruby Leung, George Em Karniadakis
Learning bias corrections for climate models using deep neural operators. CoRR (2023)
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 (2023)
Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields
Learning in latent spaces improves the predictive accuracy of deep neural operators. CoRR (2023)
Oded Ovadia, Eli Turkel, Adar Kahana, George Em Karniadakis
DiTTO: Diffusion-inspired Temporal Transformer Operator. CoRR (2023)
Varun Kumar, Leonard Gleyzer, Adar Kahana, Khemraj Shukla, George Em Karniadakis
CrunchGPT: A chatGPT assisted framework for scientific machine learning. CoRR (2023)
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 (2023)
Oded Ovadia, Adar Kahana, Panos Stinis, Eli Turkel, George Em Karniadakis
ViTO: Vision Transformer-Operator. CoRR (2023)
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 (2023)
Hanxun Jin, Enrui Zhang, Boyu Zhang, Sridhar Krishnaswamy, George Em Karniadakis, Horacio D. Espinosa
Mechanical Characterization and Inverse Design of Stochastic Architected Metamaterials Using Neural Operators. CoRR (2023)
Zheyuan Hu, Zhouhao Yang, Yezhen Wang, George Em Karniadakis, Kenji Kawaguchi
Bias-Variance Trade-off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs. CoRR (2023)
Alan John Varghese, Aniruddha Bora, Mengjia Xu, George Em Karniadakis
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers. CoRR (2023)
Sokratis J. Anagnostopoulos, Juan Diego Toscano, Nikolaos Stergiopulos, George Em Karniadakis
Residual-based attention and connection to information bottleneck theory in PINNs. CoRR (2023)
Felipe de Castro Teixeira Carvalho, Kamaljyoti Nath, Alberto Luiz Serpa, George Em Karniadakis
Learning characteristic parameters and dynamics of centrifugal pumps under multi-phase flow using physics-informed neural networks. CoRR (2023)
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 (2023)
Mario De Florio, Ioannis G. Kevrekidis, George Em Karniadakis
AI-Lorenz: A physics-data-driven framework for black-box and gray-box identification of chaotic systems with symbolic regression. CoRR (2023)
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 (2023)
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 (2023)
Yeonjong Shin, Jérôme Darbon, George Em Karniadakis
Accelerating gradient descent and Adam via fractional gradients. Neural Networks 161 (2023)
Zheyuan Hu, Khemraj Shukla, George Em Karniadakis, Kenji Kawaguchi
Tackling the Curse of Dimensionality with Physics-Informed Neural Networks. CoRR (2023)
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 (2023)
Zheyuan Hu, Zekun Shi, George Em Karniadakis, Kenji Kawaguchi
Hutchinson Trace Estimation for High-Dimensional and High-Order Physics-Informed Neural Networks. CoRR (2023)
Pengzhan Jin, Zhen Zhang, Ioannis G. Kevrekidis, George Em Karniadakis
Learning Poisson Systems and Trajectories of Autonomous Systems via Poisson Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 34 (11) (2023)
Somdatta Goswami, Ameya D. Jagtap, Hessam Babaee, Bryan T. Susi, George Em Karniadakis
Learning stiff chemical kinetics using extended deep neural operators. CoRR (2023)
Paula Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis
Leveraging Hamilton-Jacobi PDEs with time-dependent Hamiltonians for continual scientific machine learning. CoRR (2023)
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 (2023)
Zongren Zou, George Em Karniadakis
L-HYDRA: Multi-Head Physics-Informed Neural Networks. CoRR (2023)
Zongren Zou, Xuhui Meng, George Em Karniadakis
Correcting model misspecification in physics-informed neural networks (PINNs). CoRR (2023)
Vivek Oommen, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, George Em Karniadakis
Rethinking materials simulations: Blending direct numerical simulations with neural operators. CoRR (2023)
Paula Chen, Tingwei Meng, Zongren Zou, Jérôme Darbon, George Em Karniadakis
Leveraging Multi-time Hamilton-Jacobi PDEs for Certain Scientific Machine Learning Problems. CoRR (2023)
Chayan Banerjee, Kien Nguyen, Clinton Fookes, George Em Karniadakis
Physics-Informed Computer Vision: A Review and Perspectives. CoRR (2023)
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 (2023)
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. J. Comput. Phys. 493 (2023)
Alena Kopanicáková, Hardik Kothari, George Em Karniadakis, Rolf Krause
Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies. CoRR (2023)
Yue Hao, Patricio Clark Di Leoni, Olaf Marxen, Charles Meneveau, George Em Karniadakis, Tamer A. Zaki
Instability-wave prediction in hypersonic boundary layers with physics-informed neural operators. J. Comput. Sci. 73 (2023)
Simin Shekarpaz, Fanhai Zeng, George Em Karniadakis
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models. CoRR (2023)
Enrui Zhang, Bart Spronck, Jay D. Humphrey, George Em Karniadakis
G2Φnet: Relating Genotype and Biomechanical Phenotype of Tissues with Deep Learning. CoRR (2022)
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 (2022)
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) (2022)
Mitchell Daneker, Zhen Zhang, George Em Karniadakis, Lu Lu
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks. CoRR (2022)
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 (2022)
Somdatta Goswami, Aniruddha Bora, Yue Yu, George Em Karniadakis
Physics-Informed Deep Neural Operator Networks. CoRR (2022)
Vivek Oommen, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, George Em Karniadakis
Learning two-phase microstructure evolution using neural operators and autoencoder architectures. CoRR (2022)
Marta D'Elia, Hang Deng, Cedric G. Fraces, Krishna 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 (2022)
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) (2022)
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 (2022)
Ameya D. Jagtap, George Em Karniadakis
How important are activation functions in regression and classification? A survey, performance comparison, and future directions. CoRR (2022)
Khemraj Shukla, Mengjia Xu, Nathaniel Trask, George Em Karniadakis
Scalable algorithms for physics-informed neural and graph networks. CoRR (2022)
Apostolos F. Psaros, Kenji Kawaguchi, George Em Karniadakis
Meta-learning PINN loss functions. J. Comput. Phys. 458 (2022)
Kevin Linka, Amelie Schafer, Xuhui Meng, Zongren Zou, George Em Karniadakis, Ellen Kuhl
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems. CoRR (2022)
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 (2022)
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 (2022)
Minglang Yin, Enrui Zhang, Yue Yu, George Em Karniadakis
Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems. CoRR (2022)
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) (2022)
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 (2022)

George Em Karniadakis.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

George Em Karniadakis