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ORCIDParis Perdikaris
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- affiliation: University of Pennsylvania, Department of Mechanical Engineering and Applied Mechanics, Philadelphia, USA
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2020 – today
- 2024
[j22]Francisco Sahli Costabal
, Simone Pezzuto, Paris Perdikaris:
Δ-PINNs: Physics-informed neural networks on complex geometries. Eng. Appl. Artif. Intell. 127(Part B): 107324 (2024)- [j21]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): 475-498 (2024) - [i52]Sifan Wang, Bowen Li, Yuhan Chen, Paris Perdikaris:
PirateNets: Physics-informed Deep Learning with Residual Adaptive Networks. CoRR abs/2402.00326 (2024) - [i51]Leonardo Ferreira Guilhoto, Paris Perdikaris:
Composite Bayesian Optimization In Function Spaces Using NEON - Neural Epistemic Operator Networks. CoRR abs/2404.03099 (2024) - [i50]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 abs/2405.13063 (2024) - [i49]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 abs/2405.13998 (2024) - [i48]Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi Daryakenari, George Em Karniadakis:
Physics-Informed Neural Networks and Extensions. CoRR abs/2408.16806 (2024) - [i47]Shunyuan Mao, Ruobing Dong, Kwang Moo Yi, Lu Lu, Sifan Wang, Paris Perdikaris:
Disk2Planet: A Robust and Automated Machine Learning Tool for Parameter Inference in Disk-Planet Systems. CoRR abs/2409.17228 (2024) - [i46]Leonardo Ferreira Guilhoto, Paris Perdikaris:
Deep Learning Alternatives of the Kolmogorov Superposition Theorem. CoRR abs/2410.01990 (2024) - 2023
- [j20]Sifan Wang, Paris Perdikaris:
Long-time integration of parametric evolution equations with physics-informed DeepONets. J. Comput. Phys. 475: 111855 (2023) - [c7]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: 7264-7302 - [c6]Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris:
Variational Autoencoding Neural Operators. ICML 2023: 30491-30522 - [c5]Phillip Lippe, Bas Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter:
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. NeurIPS 2023 - [i45]Mohamed Aziz Bhouri, Michael Joly, Robert Yu, Soumalya Sarkar, Paris Perdikaris:
Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks. CoRR abs/2302.07260 (2023) - [i44]Jacob H. Seidman, Georgios Kissas, George J. Pappas, Paris Perdikaris:
Variational Autoencoding Neural Operators. CoRR abs/2302.10351 (2023) - [i43]Zhiwei Fang, Sifan Wang, Paris Perdikaris:
Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach. CoRR abs/2302.13143 (2023) - [i42]Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas:
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior. CoRR abs/2305.09017 (2023) - [i41]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 abs/2305.11111 (2023) - [i40]Phillip Lippe, Bastiaan S. Veeling, Paris Perdikaris, Richard E. Turner, Johannes Brandstetter:
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers. CoRR abs/2308.05732 (2023) - [i39]Sifan Wang, Shyam Sankaran, Hanwen Wang, Paris Perdikaris:
An Expert's Guide to Training Physics-informed Neural Networks. CoRR abs/2308.08468 (2023) - [i38]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 abs/2308.12939 (2023) - 2022
- [j19]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): 3957-3973 (2022) - [j18]Sifan Wang, Xinling Yu, Paris Perdikaris:
When and why PINNs fail to train: A neural tangent kernel perspective. J. Comput. Phys. 449: 110768 (2022) - [j17]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: 215:1-215:63 (2022) - [j16]Sifan Wang, Hanwen Wang, Paris Perdikaris:
Improved Architectures and Training Algorithms for Deep Operator Networks. J. Sci. Comput. 92(2): 35 (2022) - [c4]Thomas Beckers, Jacob H. Seidman, Paris Perdikaris, George J. Pappas:
Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior. CDC 2022: 1447-1453 - [c3]Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas:
NOMAD: Nonlinear Manifold Decoders for Operator Learning. NeurIPS 2022 - [i37]Georgios Kissas, Jacob H. Seidman, Leonardo Ferreira Guilhoto, Victor M. Preciado, George J. Pappas, Paris Perdikaris:
Learning Operators with Coupled Attention. CoRR abs/2201.01032 (2022) - [i36]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 abs/2201.12362 (2022) - [i35]Yibo Yang, Georgios Kissas, Paris Perdikaris:
Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors. CoRR abs/2203.03048 (2022) - [i34]Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal:
Learning cardiac activation maps from 12-lead ECG with multi-fidelity Bayesian optimization on manifolds. CoRR abs/2203.06222 (2022) - [i33]Sifan Wang, Shyam Sankaran, Paris Perdikaris:
Respecting causality is all you need for training physics-informed neural networks. CoRR abs/2203.07404 (2022) - [i32]Jacob H. Seidman, Georgios Kissas, Paris Perdikaris, George J. Pappas:
NOMAD: Nonlinear Manifold Decoders for Operator Learning. CoRR abs/2206.03551 (2022) - [i31]Arka Daw, Jie Bu, Sifan Wang, Paris Perdikaris, Anuj Karpatne:
Rethinking the Importance of Sampling in Physics-informed Neural Networks. CoRR abs/2207.02338 (2022) - [i30]Sebastian Kaltenbach, Paris Perdikaris, Phaedon-Stelios Koutsourelakis:
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems. CoRR abs/2209.02772 (2022) - [i29]Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris:
Δ-PINNs: physics-informed neural networks on complex geometries. CoRR abs/2209.03984 (2022) - [i28]Sifan Wang, Hanwen Wang, Jacob H. Seidman, Paris Perdikaris:
Random Weight Factorization Improves the Training of Continuous Neural Representations. CoRR abs/2210.01274 (2022) - 2021
- [j15]Sifan Wang, Paris Perdikaris:
Deep learning of free boundary and Stefan problems. J. Comput. Phys. 428: 109914 (2021) - [j14]Sifan Wang, Yujun Teng, Paris Perdikaris:
Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks. SIAM J. Sci. Comput. 43(5): A3055-A3081 (2021) - [c2]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: 650-658 - [c1]Rose Yu, Paris Perdikaris, Anuj Karpatne:
Physics-Guided AI for Large-Scale Spatiotemporal Data. KDD 2021: 4088-4089 - [i27]Yibo Yang, Antoine Blanchard, Themistoklis P. Sapsis, Paris Perdikaris:
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs. CoRR abs/2102.10085 (2021) - [i26]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 abs/2102.10863 (2021) - [i25]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 abs/2103.03385 (2021) - [i24]Sifan Wang, Hanwen Wang, Paris Perdikaris:
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets. CoRR abs/2103.10974 (2021) - [i23]Sifan Wang, Paris Perdikaris:
Long-time integration of parametric evolution equations with physics-informed DeepONets. CoRR abs/2106.05384 (2021) - [i22]Sifan Wang, Hanwen Wang, Paris Perdikaris:
Improved architectures and training algorithms for deep operator networks. CoRR abs/2110.01654 (2021) - [i21]Sifan Wang, Mohamed Aziz Bhouri, Paris Perdikaris:
Fast PDE-constrained optimization via self-supervised operator learning. CoRR abs/2110.13297 (2021) - [i20]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 abs/2112.08075 (2021) - 2020
- [i19]Sifan Wang, Yujun Teng, Paris Perdikaris:
Understanding and mitigating gradient pathologies in physics-informed neural networks. CoRR abs/2001.04536 (2020) - [i18]Songsong Ji, Gang Pang, Jiwei Zhang, Yibo Yang, Paris Perdikaris:
Exact artificial boundary conditions of 1D semi-discretized peridynamics. CoRR abs/2002.12846 (2020) - [i17]Yibo Yang, Mohamed Aziz Bhouri, Paris Perdikaris:
Bayesian differential programming for robust systems identification under uncertainty. CoRR abs/2004.06843 (2020) - [i16]Sifan Wang, Paris Perdikaris:
Deep learning of free boundary and Stefan problems. CoRR abs/2006.05311 (2020) - [i15]Sifan Wang, Xinling Yu, Paris Perdikaris:
When and why PINNs fail to train: A neural tangent kernel perspective. CoRR abs/2007.14527 (2020) - [i14]Brandon Reyes, Amanda A. Howard, Paris Perdikaris, Alexandre M. Tartakovsky:
Learning Unknown Physics of non-Newtonian Fluids. CoRR abs/2009.01658 (2020) - [i13]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 abs/2012.10047 (2020)
2010 – 2019
- 2019
- [j13]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: 686-707 (2019) - [j12]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: 56-81 (2019) - [j11]Yibo Yang, Paris Perdikaris:
Adversarial uncertainty quantification in physics-informed neural networks. J. Comput. Phys. 394: 136-152 (2019) - [j10]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) - [j9]Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Machine Learning of Space-Fractional Differential Equations. SIAM J. Sci. Comput. 41(4): A2485-A2509 (2019) - [i12]Yibo Yang, Paris Perdikaris:
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems. CoRR abs/1901.04878 (2019) - [i11]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 abs/1901.06314 (2019) - [i10]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 abs/1904.04058 (2019) - [i9]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 abs/1905.03406 (2019) - [i8]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 abs/1905.04817 (2019) - 2018
- [j8]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) - [i7]Mamikon A. Gulian, Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Machine Learning of Space-Fractional Differential Equations. CoRR abs/1808.00931 (2018) - [i6]Yibo Yang, Paris Perdikaris:
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks. CoRR abs/1811.04026 (2018) - [i5]Yibo Yang, Paris Perdikaris:
Physics-informed deep generative models. CoRR abs/1812.03511 (2018) - 2017
- [j7]Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Inferring solutions of differential equations using noisy multi-fidelity data. J. Comput. Phys. 335: 736-746 (2017) - [j6]Lucia Parussini, Daniele Venturi, Paris Perdikaris, George E. Karniadakis:
Multi-fidelity Gaussian process regression for prediction of random fields. J. Comput. Phys. 336: 36-50 (2017) - [j5]Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Machine learning of linear differential equations using Gaussian processes. J. Comput. Phys. 348: 683-693 (2017) - [j4]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: 694-714 (2017) - [i4]Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations. CoRR abs/1703.10230 (2017) - [i3]Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations. CoRR abs/1711.10561 (2017) - [i2]Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations. CoRR abs/1711.10566 (2017) - 2016
- [j3]Yue Yu, Paris Perdikaris, George E. Karniadakis:
Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms. J. Comput. Phys. 323: 219-242 (2016) - [j2]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: 9-16 (2016) - [j1]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) - [i1]Maziar Raissi, Paris Perdikaris, George E. Karniadakis:
Inferring solutions of differential equations using noisy multi-fidelity data. CoRR abs/1607.04805 (2016)
Coauthor Index
George Em Karniadakis
aka: George E. Karniadakis
aka: George E. Karniadakis
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last updated on 2024-11-08 20:25 CET by the dblp team
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