Paris Perdikaris

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 78

Top Topics

Differential Equations

Gaussian Process

Partially Observed

Top Venues

J. Comput. Phys.

SIAM J. Sci. Comput.

Publications

Sifan Wang, Jacob H. Seidman, Shyam Sankaran, Hanwen Wang, George J. Pappas, Paris Perdikaris
Bridging Operator Learning and Conditioned Neural Fields: A Unifying Perspective. CoRR (2024)
Zhiwei Fang, Sifan Wang, Paris Perdikaris
Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries. Neural Comput. 36 (3) (2024)
Leonardo Ferreira Guilhoto, Paris Perdikaris
Composite Bayesian Optimization In Function Spaces Using NEON - Neural Epistemic Operator Networks. CoRR (2024)
Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
Δ-PINNs: Physics-informed neural networks on complex geometries. Eng. Appl. Artif. Intell. 127 (Part B) (2024)
Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks. CoRR (2024)
Cristian Bodnar, Wessel P. Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A. Weyn, Haiyu Dong, Anna Vaughan, Jayesh K. Gupta, Kit Thambiratnam, Alex Archibald, Elizabeth Heider, Max Welling, Richard E. Turner, Paris Perdikaris
Aurora: A Foundation Model of the Atmosphere. CoRR (2024)
Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris
Variational Autoencoding Neural Operators. ICML (2023)
Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne
Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling. ICML (2023)
Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior. CoRR (2023)
Zhiwei Fang, Sifan Wang, Paris Perdikaris
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach. CoRR (2023)
Sifan Wang, Shyam Sankaran, Hanwen Wang, Paris Perdikaris
An Expert's Guide to Training Physics-informed Neural Networks. CoRR (2023)
Zhiwei Fang, Sifan Wang, Paris Perdikaris
Learning Only On Boundaries: a Physics-Informed Neural operator for Solving Parametric Partial Differential Equations in Complex Geometries. CoRR (2023)
Sifan Wang, Paris Perdikaris
Long-time integration of parametric evolution equations with physics-informed DeepONets. J. Comput. Phys. 475 (2023)
Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR (2023)
Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris
Variational Autoencoding Neural Operators. CoRR (2023)
Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS (2023)
Mohamed Aziz Bhouri, Michael Joly, Robert Yu, Soumalya Sarkar, Paris Perdikaris
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks. CoRR (2023)
Shunyuan Mao, Ruobing Dong, Lu Lu, Kwang Moo Yi, Sifan Wang, Paris Perdikaris
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems. CoRR (2023)
Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior. CDC (2022)
Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal
Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds. CoRR (2022)
Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. CoRR (2022)
Yibo Yang, Georgios Kissas, Paris Perdikaris
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors. CoRR (2022)
Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne
Rethinking the Importance of Sampling in Physics-informed Neural Networks. CoRR (2022)
Sifan Wang, Hanwen Wang, Paris Perdikaris
Improved Architectures and Training Algorithms for Deep Operator Networks. J. Sci. Comput. 92 (2) (2022)
Sifan Wang, Shyam Sankaran, Paris Perdikaris
Respecting causality is all you need for training physics-informed neural networks. CoRR (2022)
Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. CoRR (2022)
Sifan Wang, Xinling Yu, Paris Perdikaris
When and why PINNs fail to train: A neural tangent kernel perspective. J. Comput. Phys. 449 (2022)
Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas
NOMAD: Nonlinear Manifold Decoders for Operator Learning. CoRR (2022)
Sifan Wang, Hanwen Wang, Jacob H. Seidman, Paris Perdikaris
Random Weight Factorization Improves the Training of Continuous Neural Representations. CoRR (2022)
Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris
Learning Operators with Coupled Attention. J. Mach. Learn. Res. 23 (2022)
Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas
NOMAD: Nonlinear Manifold Decoders for Operator Learning. NeurIPS (2022)
Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
Δ-PINNs: physics-informed neural networks on complex geometries. CoRR (2022)
Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris
Learning Operators with Coupled Attention. CoRR (2022)
Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. Eng. Comput. 38 (5) (2022)
Rose Yu, Paris Perdikaris, Anuj Karpatne
Physics-Guided AI for Large-Scale Spatiotemporal Data. KDD (2021)
Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks. CoRR (2021)
Yibo Yang, Antoine Blanchard, Themistoklis P. Sapsis, Paris Perdikaris
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs. CoRR (2021)
Sifan Wang, Hanwen Wang, Paris Perdikaris
Improved architectures and training algorithms for deep operator networks. CoRR (2021)
Lia Gander, Simone Pezzuto, Ali Gharaviri, Rolf Krause, Paris Perdikaris, Francisco Sahli Costabal
Fast characterization of inducible regions of atrial fibrillation models with multi-fidelity Gaussian process classification. CoRR (2021)
Sifan Wang, Paris Perdikaris
Long-time integration of parametric evolution equations with physics-informed DeepONets. CoRR (2021)
Sifan Wang, Hanwen Wang, Paris Perdikaris
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets. CoRR (2021)
Sifan Wang, Paris Perdikaris
Deep learning of free boundary and Stefan problems. J. Comput. Phys. 428 (2021)
Mohamed Aziz Bhouri, Paris Perdikaris
Gaussian processes meet NeuralODEs: A Bayesian framework for learning the dynamics of partially observed systems from scarce and noisy data. CoRR (2021)
Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris
Fast PDE-constrained optimization via self-supervised operator learning. CoRR (2021)
Sifan Wang, Yujun Teng, Paris Perdikaris
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks. SIAM J. Sci. Comput. 43 (5) (2021)
Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks. FIMH (2021)
Sifan Wang, Hanwen Wang, Paris Perdikaris
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks. CoRR (2020)
Sifan Wang, Xinling Yu, Paris Perdikaris
When and why PINNs fail to train: A neural tangent kernel perspective. CoRR (2020)
Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky
Learning Unknown Physics of non-Newtonian Fluids. CoRR (2020)
Sifan Wang, Paris Perdikaris
Deep learning of free boundary and Stefan problems. CoRR (2020)
Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris
Bayesian differential programming for robust systems identification under uncertainty. CoRR (2020)
Songsong Ji, Gang Pang, Jiwei Zhang, Yibo Yang, Paris Perdikaris
Exact artificial boundary conditions of 1D semi-discretized peridynamics. CoRR (2020)
Sifan Wang, Yujun Teng, Paris Perdikaris
Understanding and mitigating gradient pathologies in physics-informed neural networks. CoRR (2020)
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data. J. Comput. Phys. 394 (2019)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378 (2019)
Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Machine Learning of Space-Fractional Differential Equations. SIAM J. Sci. Comput. 41 (4) (2019)
Yinhao Zhu, Nicholas Zabaras, Phaedon-Stelios Koutsourelakis, Paris Perdikaris
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data. CoRR (2019)
Yibo Yang, Paris Perdikaris
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems. CoRR (2019)
Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R. Witschey, John A. Detre, Paris Perdikaris
Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning. CoRR (2019)
Yibo Yang, Paris Perdikaris
Adversarial uncertainty quantification in physics-informed neural networks. J. Comput. Phys. 394 (2019)
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
Integrating machine learning and multiscale modeling - perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digit. Medicine 2 (2019)
Ramakrishna Tipireddy, Paris Perdikaris, Panos Stinis, Alexandre M. Tartakovsky
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations. CoRR (2019)
Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models. CoRR (2019)
Yibo Yang, Paris Perdikaris
Physics-informed deep generative models. CoRR (2018)
Yibo Yang, Paris Perdikaris
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks. CoRR (2018)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations. SIAM J. Sci. Comput. 40 (1) (2018)
Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Machine Learning of Space-Fractional Differential Equations. CoRR (2018)
Lucia Parussini, Daniele Venturi, Paris Perdikaris, George E. Karniadakis
Multi-fidelity Gaussian process regression for prediction of random fields. J. Comput. Phys. 336 (2017)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations. CoRR (2017)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations. CoRR (2017)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Inferring solutions of differential equations using noisy multi-fidelity data. J. Comput. Phys. 335 (2017)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Machine learning of linear differential equations using Gaussian processes. J. Comput. Phys. 348 (2017)
Guofei Pang, Paris Perdikaris, Wei Cai, George E. Karniadakis
Discovering variable fractional orders of advection-dispersion equations from field data using multi-fidelity Bayesian optimization. J. Comput. Phys. 348 (2017)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations. CoRR (2017)
Yue Yu, Paris Perdikaris, George E. Karniadakis
Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms. J. Comput. Phys. 323 (2016)
Maziar Raissi, Paris Perdikaris, George E. Karniadakis
Inferring solutions of differential equations using noisy multi-fidelity data. CoRR (2016)
Paris Perdikaris, Daniele Venturi, George E. Karniadakis
Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets. SIAM J. Sci. Comput. 38 (4) (2016)
Paris Perdikaris, Joseph A. Insley, Leopold Grinberg, Yue Yu, Michael E. Papka, George E. Karniadakis
Visualizing multiphysics, fluid-structure interaction phenomena in intracranial aneurysms. Parallel Comput. 55 (2016)