Paris Perdikaris

According to our database, Paris Perdikaris authored at least 58 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Δ-PINNs: Physics-informed neural networks on complex geometries.

Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris

Eng. Appl. Artif. Intell., January, 2024

PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks.

Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris

CoRR, 2024

2023

Long-time integration of parametric evolution equations with physics-informed DeepONets.

Sifan Wang, Paris Perdikaris

J. Comput. Phys., February, 2023

Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries.

Zhiwei Fang, Sifan Wang, Paris Perdikaris

CoRR, 2023

An Expert's Guide to Training Physics-informed Neural Networks.

Sifan Wang, Shyam Sankaran, Hanwen Wang, Paris Perdikaris

CoRR, 2023

PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems.

Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi, Sifan Wang, Paris Perdikaris

CoRR, 2023

Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach.

Zhiwei Fang, Sifan Wang, Paris Perdikaris

CoRR, 2023

Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks.

Mohamed Aziz Bhouri, Michael Joly, Robert Yu, Soumalya Sarkar, Paris Perdikaris

CoRR, 2023

PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers.

Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Variational Autoencoding Neural Operators.

Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris

Proceedings of the International Conference on Machine Learning, 2023

Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling.

Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne

Proceedings of the International Conference on Machine Learning, 2023

2022

Improved Architectures and Training Algorithms for Deep Operator Networks.

Sifan Wang, Hanwen Wang, Paris Perdikaris

J. Sci. Comput., 2022

Learning Operators with Coupled Attention.

Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris

J. Mach. Learn. Res., 2022

When and why PINNs fail to train: A neural tangent kernel perspective.

Sifan Wang, Xinling Yu, Paris Perdikaris

J. Comput. Phys., 2022

Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps.

Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto

Eng. Comput., 2022

Random Weight Factorization Improves the Training of Continuous Neural Representations.

Sifan Wang, Hanwen Wang, Jacob H. Seidman, Paris Perdikaris

CoRR, 2022

Semi-supervised Invertible DeepONets for Bayesian Inverse Problems.

Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis

CoRR, 2022

Rethinking the Importance of Sampling in Physics-informed Neural Networks.

Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne

CoRR, 2022

Respecting causality is all you need for training physics-informed neural networks.

Sifan Wang, Shyam Sankaran, Paris Perdikaris

CoRR, 2022

Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds.

Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal

CoRR, 2022

Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors.

Yibo Yang, Georgios Kissas, Paris Perdikaris

CoRR, 2022

NOMAD: Nonlinear Manifold Decoders for Operator Learning.

Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior.

Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas

Proceedings of the 61st IEEE Conference on Decision and Control, 2022

2021

Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks.

Sifan Wang, Yujun Teng, Paris Perdikaris

SIAM J. Sci. Comput., 2021

Deep learning of free boundary and Stefan problems.

Sifan Wang, Paris Perdikaris

J. Comput. Phys., 2021

Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification.

Lia Gander, Simone Pezzuto, Ali Gharaviri, Rolf Krause, Paris Perdikaris, Francisco Sahli Costabal

CoRR, 2021

Fast PDE-constrained optimization via self-supervised operator learning.

Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris

CoRR, 2021

Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets.

Sifan Wang, Hanwen Wang, Paris Perdikaris

CoRR, 2021

Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data.

Mohamed Aziz Bhouri, Paris Perdikaris

CoRR, 2021

Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs.

Yibo Yang, Antoine Blanchard, Themistoklis P. Sapsis, Paris Perdikaris

CoRR, 2021

Physics-Guided AI for Large-Scale Spatiotemporal Data.

Rose Yu, Paris Perdikaris, Anuj Karpatne

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks.

Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause

Proceedings of the Functional Imaging and Modeling of the Heart, 2021

2020

On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks.

Sifan Wang, Hanwen Wang, Paris Perdikaris

CoRR, 2020

Learning Unknown Physics of non-Newtonian Fluids.

Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky

CoRR, 2020

Bayesian differential programming for robust systems identification under uncertainty.

Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris

CoRR, 2020

Exact artificial boundary conditions of 1D semi-discretized peridynamics.

Songsong Ji, Gang Pang, Jiwei Zhang, Yibo Yang, Paris Perdikaris

CoRR, 2020

Understanding and mitigating gradient pathologies in physics-informed neural networks.

Sifan Wang, Yujun Teng, Paris Perdikaris

CoRR, 2020

2019

Machine Learning of Space-Fractional Differential Equations.

Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis

SIAM J. Sci. Comput., 2019

Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences.

Mark S. Alber, Adrian Buganza Tepole, William R. Cannon, Suvranu De, Salvador Dura-Bernal, Krishna C. Garikipati, George E. Karniadakis, William W. Lytton, Paris Perdikaris, Linda R. Petzold, Ellen Kuhl

npj Digit. Medicine, 2019

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data.

Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris

J. Comput. Phys., 2019

Adversarial uncertainty quantification in physics-informed neural networks.

Yibo Yang, Paris Perdikaris

J. Comput. Phys., 2019

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2019

Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning.

Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R. Witschey, John A. Detre, Paris Perdikaris

CoRR, 2019

Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models.

Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado

CoRR, 2019

A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations.

Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky

CoRR, 2019

Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems.

Yibo Yang, Paris Perdikaris

CoRR, 2019

2018

Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

SIAM J. Sci. Comput., 2018

Physics-informed deep generative models.

Yibo Yang, Paris Perdikaris

CoRR, 2018

2017

Machine learning of linear differential equations using Gaussian processes.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2017

Inferring solutions of differential equations using noisy multi-fidelity data.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2017

Multi-fidelity Gaussian process regression for prediction of random fields.

Lucia Parussini, Daniele Venturi, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2017

Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization.

Guofei Pang, Paris Perdikaris, Wei Cai, George E. Karniadakis

J. Comput. Phys., 2017

Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

CoRR, 2017

Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

CoRR, 2017

Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations.

Maziar Raissi, Paris Perdikaris, George E. Karniadakis

CoRR, 2017

2016

Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets.

Paris Perdikaris, Daniele Venturi, George E. Karniadakis

SIAM J. Sci. Comput., 2016

Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms.

Paris Perdikaris, Joseph A. Insley, Leopold Grinberg, Yue Yu, Michael E. Papka, George E. Karniadakis

Parallel Comput., 2016

Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms.

Yue Yu, Paris Perdikaris, George E. Karniadakis

J. Comput. Phys., 2016